MTS BigCommerce AI Commerce Intelligence
MTS BigCommerce AI Commerce Intelligence
Brand: MageTech Solutions
SKU: MTS-BC-INTEL-001
A working, multi-tenant commerce intelligence platform for BigCommerce — ten modules, explainable AI, a real OAuth/webhook integration, and a delivery model built for weeks rather than quarters.
MTS BigCommerce AI Commerce Intelligence
A finished product, not a proposal. A working, multi-tenant commerce intelligence platform for BigCommerce — plus the engineering team that built it, can extend it, can maintain it, and can build the next version from scratch.
What it is
A multi-tenant analytics and decision platform for BigCommerce merchants: sales, customers, products, inventory and marketing in one warehouse, with explainable AI insights, threshold alerts, an AI copilot, and scheduled reports — served through a role-aware web application.
You can log in today and use every module. This is the reference implementation of how we work: structured, opinionated, complete and running.
The Platform at a Glance
10
Product modules64
REST endpoints32
Data models7
Access roles12
Queues & schedules10
Automated E2E testsSeeded demo workspace: 1,284 products, 12,450 customers, 8,420 orders, 21,160 order lines, 366 daily snapshots, $284.5K revenue, 15 campaigns, 7 generated insights and 5 alert rules.
The Problem We Solve
BigCommerce merchants have the data but not the answers. The control panel reports what happened; nobody joins sales, customers, stock and marketing into one decision view.
| Where it hurts today | What it costs | What this platform does |
|---|---|---|
| Sales live in the control panel, stock in a grid, marketing in ad tools | Decisions made on a single slice of the business | One warehouse, one joined view across all ten modules |
| Stockouts and churn discovered after the revenue is gone | Lost margin and repeat customers | Daily insight engine, coverage maths, at-risk segments, threshold alerts |
| Manual weekly exports rebuilt by hand | Hours of labour, inconsistent numbers, no audit trail | Parameterised CSV reports, scheduled delivery, stored history |
| Generic AI tools with no access to store data | Confident answers with invented numbers | Deterministic rules first, optional LLM explanation second, every figure traceable |
| Merchant data leaving the business for third-party AI | Privacy exposure and compliance risk | AI_PROVIDER=rule runs fully local; strict privacy mode blocks context sharing |
| Nobody can answer "who changed this, and when?" | Trust and audit failures | Role-based access, tenant isolation, and a full audit trail with actor and IP |
Who feels it
Store owner — wants the truth
One number for revenue, orders, margin risk and customer value — without waiting a week for a report.
Operations — wants warning
Coverage, low-stock and reorder signals early enough to act on, per variant and per location.
Marketing — wants proof
Campaign spend, attributed revenue and ROAS shown against store-wide performance, not in isolation.
Finance — wants records
Exports that reconcile, roles that restrict access, and an audit trail that satisfies a question months later.
The Application, Module by Module
Ten modules, one design language, one data layer, one permission model. Each module has its own accent colour, so the interface tells you where you are before you read a word.
Dashboard — the 60-second view
Revenue, orders, AOV, customers, low-stock and open-alert counters; trends; channel mix; top products; latest AI insights; recent activity. What the owner opens first every morning.
Sales — revenue quality
Pipeline by status, revenue trend against the previous period, channel contribution, AOV movement, and order-level detail with customer context.
Customers — segments and retention
VIP, high-value, at-risk and inactive segments with counts and value at stake; lifetime value distribution; win-back targeting.
Products — catalogue health
Performance by revenue and units, status mix, category and brand breakdowns, price and margin view, and a product health score for assortment decisions.
Inventory — stock coverage
Days-remaining coverage, low-stock and out-of-stock queues, reorder points, stock trends and valuation, with variant-level drill-down.
Marketing — campaign ROI
Spend, attributed revenue, ROAS, CTR and conversion per campaign, channel efficiency comparison, and revenue trend in campaign context.
Insights — intelligence & copilot
Generated insights with evidence and severity, prioritised recommendations, full history, and the AI copilot for natural-language questions.
Reports — exports and schedules
Sales, inventory, customer and full-dataset reports, parameterised date ranges, CSV download, scheduled delivery and generation history.
Alerts — threshold monitoring
Rules for revenue drops, order anomalies, AOV shifts, low stock and churn; triggered alerts with metric, value and threshold; in-app notification feed.
Settings — administration
BigCommerce connection and sync history, team and roles, subscription and plan, AI provider configuration, notification preferences, audit log.
What runs underneath every module
BigCommerce sync
OAuth install or API-account connect; paged, resumable sync of categories, brands, products, customers, orders, inventory and settings; signed webhooks; visible checkpoints.
Analytics engine
Shared metric computation — revenue, orders, AOV, growth, channel mix, stock cover, customer aggregates — used identically by the API, the insight engine and reports.
Background automation
Five job queues and five schedules: dispatch syncs, nightly full sync, daily insights, alert sweeps and report delivery. Nothing heavy runs on a user request.
Governance
Seven roles, 25 permissions, tenant-scoped data access, hashed session tokens, encrypted integration secrets, and an audit trail of every administrative action.
Who Sees What
One platform, seven operating personas, each with a scoped view. This is what makes the product safe to give to a whole team on day one — and enforcement is server-side, not hidden buttons.
| Role | Scope |
|---|---|
| Owner | Everything, including team, plan and integration administration. The only role that can change ownership-level settings. |
| Admin | All analytics, alerts and reports; manages the team and alert rules. The operations or IT lead. |
| Analyst | Full read access across modules; generates insights and reports. The person who interrogates the data. |
| Sales manager | Sales, customers, products, reports and alerts. Revenue quality and pipeline focus. |
| Operations manager | Products, inventory, alerts and reports. Stock coverage and fulfilment focus. |
| Marketing manager | Marketing, sales, dashboard, reports. Campaign efficiency and growth focus. |
| Viewer | Read-only dashboards and report downloads — the safe seat for finance, agencies and stakeholders. |
How We Present the Demo
Thirty minutes, screen-shared, on a seeded workspace. No slide deck until the last five minutes. We log in live — including the role switch — so you see authentication and access control rather than being told about them.
| # | Screen | Proof point |
|---|---|---|
| 1 | Marketing site & login | Branding, product credibility, real authentication |
| 2 | Dashboard | Speed to answer, and AI already summarised |
| 3 | Sales | Data integrity and order-level drill-down |
| 4 | Customers | Retention is measurable and targeted |
| 5 | Inventory | Operations value, variant-level detail |
| 6 | Marketing | Attribution joined to real revenue |
| 7 | Insights | Explainable AI, not a black box |
| 8 | AI copilot | Grounded AI with usage metering |
| 9 | Role switch | Enterprise readiness — enforced on the server |
| 10 | Reports | Deliverable artefacts, not dashboards |
| 11 | Alerts | Automation and proactive operation |
| 12 | Settings → BigCommerce | Real integration, not an import script |
| 13 | Settings → Team & audit | Procurement and compliance confidence |
| 14 | Close | A decision with a date, not a follow-up email |
Demo ground rules: never show a feature that is not built; always show at least one role restriction and one export; always end with a number — days to a working pilot; and state known limits before they are discovered.
Deliverables on Handover
What you physically receive when the project is done. Nothing is implied, nothing is "available on request".
01 · The working application
- All ten modules live on your infrastructure
- Your BigCommerce store connected and syncing
- Your roles, users and permissions configured
- Alert rules and schedules tuned to your business
- AI provider and privacy mode set as agreed
- Reporting verified against the control panel
02 · The source code
- Full monorepo with history and sensible structure
- Web application, API, worker and shared packages
- Database schema and all migrations
- Documented environment definition and configuration
- No proprietary lock-in or obfuscated components
03 · The delivery pipeline
- CI running build, lint, type and browser tests on every push
- Container definitions for web, API, worker, database and queue
- Deployment runbook and rollback procedure
- Backup, restore and monitoring configuration
04 · The documentation
- This customer and demo document
- Technical architecture document
- API reference and data model reference
- Development and deployment guide
- Administrator guide for roles, settings and alerts
- Written scope and decision log for custom work
05 · The training
- Role-based walkthroughs per module
- Administrator session: users, roles, rules, plans
- AI usage and governance session
- Handover session with your technical and finance teams
- Recorded walkthroughs for future team members
06 · The data and reporting
- Verified historical backfill as agreed
- Reconciliation report against source figures
- Scheduled report definitions and delivery
- Data dictionary for every entity in the warehouse
- Export paths and retention explained
07 · Security and governance
- Hardening checklist completed before internet exposure
- Secrets inventory and rotation procedure
- Role and permission matrix signed off
- Audit log configured and reviewed
- Security summary for your procurement process
08 · The support terms
- Chosen support model: self-managed, advisory or managed
- Agreed response and resolution expectations
- Escalation path and named contacts
- Roadmap review cadence
- Upgrade and release procedure
09 · The support plan
- Success criteria agreed before go-live
- 90-day hypercare with proactive check-ins
- Quarterly reviews against outcomes
- Prioritised roadmap for your requests
Handover rule
Nothing in this list is "phase two of the engagement" unless you agree it in writing. When we say delivered, the item is running in your environment, documented, and understood by your team.
Develop · Maintain · Build From Scratch
Three distinct kinds of work. Most vendors only sell the first and subcontract the third. We do all three with the same team.
Develop — extend what exists
New analytics modules, reports, alert types, dashboard widgets, integration entities, custom roles, API endpoints and copilot tool calls — added into the same architecture and design system so the product stays coherent.
- Custom modules and vertical packs
- New data sources and sync entities
- Forecasting and scenario modelling
- Approval workflows and multi-entity reporting
Maintain — keep it healthy
Bug fixes, dependency upgrades, platform API changes, performance work, monitoring, backups, incident response, security patches and quarterly health reviews.
- BigCommerce and dependency drift
- Proactive monitoring and alerting
- Backup, restore and disaster drills
- Capacity and cost optimisation
From scratch — start at zero
Entirely new products and platforms: discovery, architecture, UX, engineering, delivery and support — the same discipline that produced this platform.
- New SaaS and internal platforms
- Custom commerce and ERP integrations
- AI assistants and automation tooling
- Internal analytics and reporting systems
The work we are asked for most often
| Ask | What it looks like in practice | Typical shape |
|---|---|---|
| "We already have a BI tool" | Export paths and a data dictionary feed their warehouse; we add the commerce-specific logic they cannot model | Integration · days to weeks |
| "Our catalogue logic is unusual" | Custom product health, bundle and variant analysis, pricing and margin models on top of the existing schema | Custom module |
| "Alerts must reach our team" | Email or chat delivery for the existing alert engine, with tenant-scoped preferences | Integration |
| "We run several stores" | Multi-store tenants, per-store scoping, consolidated reporting and consolidated AI | Platform extension |
| "Sell this under our brand" | White-label: logos, colours, domain, copy, and tenant-isolated tenants for their customers | Reseller programme |
| "Prove value first" | Time-boxed pilot on your data with agreed success criteria and a written go/no-go | Pilot |
| "Replace an old system" | Legacy audit, migration plan, strangler rollout, parallel run and decommissioning | Modernisation |
Our Service Catalogue
The full capability set behind the platform. Any of these can be engaged standalone or as part of a delivery.
Workshops, process mapping, data and integration audits, target architecture, written scope with success criteria.
Information architecture, wireframes, visual design, a documented design system, accessibility and responsive behaviour.
Server rendering, component systems, form and state management, performance budgets.
REST and service design, auth, validation, background jobs, caching, error contracts, integration endpoints.
Schema design, migrations, backfills, metric layers, data quality checks, snapshot and aggregation strategy, export pipelines.
App development, OAuth and token integrations, V2/V3 API clients, webhooks, catalogue and inventory sync, app-listing readiness.
Insight engines, copilot tool-calling, privacy and budget governance, provider selection, output evaluation, workflow automation.
Report definitions, scheduled delivery, export formats, executive dashboards, finance-grade reconciliation.
Containerisation, CI/CD, infrastructure as code, environment management, database and queue operations, backups, cost control.
Test strategy, unit and integration coverage, browser end-to-end suites, regression discipline, release verification.
Threat modelling, session hardening, secret management, access reviews, audit readiness, dependency scanning.
Monitoring, incident response, upgrades, patches, performance tuning, a named contact with agreed response expectations.
Auditing old systems, incremental replacement behind a facade, data migration with verification, decommissioning.
Rebranding, custom domains, tenant-isolated deployments for your customers, agency and store-group packaging.
Role-based sessions, recorded walkthroughs and an administrator guide, so adoption is not dependent on us.
Our Technology Expertise
We are a TypeScript-first engineering team working across the modern commerce stack. This is the toolset behind the platform you just saw — and the toolset we bring to your project.
| Discipline | Technologies | How we apply it |
|---|---|---|
| Language | TypeScript 5 (strict), modern JavaScript, SQL | One language across front end, back end, worker and shared packages; types as the contract |
| Web front end | Next.js 15 App Router, React 19, Tailwind CSS 4, SVG data visualisation | Server rendering for speed and SEO, client components only where interaction demands it |
| Backend | NestJS 11, REST API design, guards and interceptors, Zod validation | Thin controllers, service-layer business rules, consistent error contracts |
| Data | PostgreSQL 16, Prisma 6, migrations, indexing, aggregation | Normalised multi-tenant schema, pre-aggregated snapshots, migrations reviewed as SQL |
| Queues & jobs | Redis 7, BullMQ 5, repeatable schedulers | All long-running work off the request path, with retries and job retention |
| Commerce platforms | BigCommerce APIs v2/v3, OAuth 2.0, API accounts, webhooks, catalogue and inventory models | Rate-limit-aware clients, resumable sync, canonical channel mapping, signed callbacks |
| AI engineering | Rule engines, retrieval and tool-calling patterns, OpenAI / Anthropic / Gemini adapters, usage metering | Deterministic findings first, models as an explainability layer, privacy modes and budgets |
| Platform & DevOps | Docker, GitHub Actions, Turborepo monorepos, environment-driven configuration | Dependency-ordered builds, CI gates, reproducible environments, no secrets in the repository |
| Quality | Playwright, ESLint 9, static type gates, load and performance testing | Three quality layers and a release gate that blocks on regressions |
| Design systems | Token architecture, component libraries, brand theming, accessible interaction patterns | Colour, type, spacing and motion defined once and reused everywhere |
We design before we demo. We build reusable cores, not copy-paste per feature. We version everything. We automate the boring parts. We test the parts that would cost money if broken — authentication, permissions, data integrity and exports. We document as we go.
How We Differ
| What matters to you | Generic agency | Freelancer / small team | SaaS vendor | Big consultancy | MageTech Solutions |
|---|---|---|---|---|---|
| Time to a working version | Slow, discovery-heavy | Quick but narrow | Immediate, not yours | Very slow, heavy governance | Days for a pilot, weeks for production — the core platform already exists |
| You own the code | Sometimes, reluctantly | Yes | No, subscription only | Yes, as an asset you maintain | Yes, always — source, docs, CI, runbook |
| Customisation | Good | Good, if the skill exists | Only via extensions | Excellent and expensive | Good, on a platform architected for it |
| Speed after launch | Slow without budget | Depends on availability | Vendor roadmap only | Slow | Fast — the same team that built it |
| Maintenance & support | Extra contract | Ad hoc | Included, generic | Separate, expensive | Included options with agreed response terms |
| AI safe for your data | Unclear | Usually a public API | Vendor policy | Policy decks | Local-first rule engine, optional models, privacy modes and metering |
| Cost profile | High, unpredictable | Cheap, risky at scale | Recurring, no ownership | Highest | Platform subscription plus scoped work — no lock-in |
| Who does the work | Rotating staff | One or two people | N/A | Many juniors, few builders | Specialists who own the outcome end to end |
Our six commitments
- We ship a product, not a promise. Your pilot is a configuration and connection exercise, not a nine-month build.
- You own everything. Code, documentation, infrastructure definition, CI. If we ever stop working together, you keep a product that still runs.
- The same team throughout. The people who architect and build the platform are the people who train your team and support it afterwards.
- Explainable by design. Deterministic first — a finding with its evidence beats a clever sentence that cannot be defended to your board.
- A platform, not a project. Every engagement lands on a shared foundation: authentication, roles, design system, warehouse, jobs, CI. The second feature is cheaper than the first.
- We tell you what is not built. Known limits are in our documentation, not discovered in month three.
How Fast We Deliver
Speed here is not heroics. It comes from starting on a finished platform, and from parallel work with a clear definition of done.
Day 1–2
Workshops and access — goals, data scope, roles, environment and credentials collected in one session.Day 3–5
Connection and first sync — BigCommerce connected, first data verified against the control panel.Day 6–10
Configured workspace — roles, users, alert rules, thresholds, report definitions and AI policy set with you.Week 2
Live demo on your data and a go/no-go — success criteria reviewed in writing.Week 3–4
Backfill, hardening and training — history loaded, hardening applied, role-based training delivered.Week 5–6
Go-live and hypercare — production cutover, monitoring, documentation handover, 90-day hypercare starts.Ongoing
Managed support and roadmap — monitoring, upgrades, quarterly reviews, prioritised enhancements.Assumptions
Single store or store group, an accessible API account, and a decision-maker available weekly. We tell you before kickoff if this changes.Where the time actually goes: integration and data verification 18% · configuration, roles and alert tuning 14% · backfill, reconciliation and hardening 22% · training, documentation and handover 26% · custom scope, if any, 20%. Note what is not in the list: months of groundwork.
Our Standards
When we add anything to a MageTech product, it follows the same path. Consistency is why delivery is fast and why the product stays coherent as it grows.
Schema in the database package, rules in a shared package, a thin controller plus service in the API, a processor if asynchronous, a page plus components in the web app, an end-to-end assertion, and documentation in the same change.
Add the module to the brand map and the navigation, header accent, stat chips and charts pick it up automatically.
Compose from the existing card, stat, header, table and chart components so the interface stays one system.
Module colour carries identity; the orange gradient is reserved for the primary conversion action; indigo and cyan always mean AI.
Every endpoint declares its permission; the browser is never trusted for identity, tenant or data access.
Type check, lint, run the browser suite against a seeded database, and verify background behaviour after any schema, queue or analytics change.
Every release includes a reviewed database migration, an API contract kept in step, green type and lint gates, browser end-to-end tests, updated documentation, an audit-visible change record and a rollback path. When we white-label or extend the platform for you, these rules become your product's rules.
Engagement Models
Four ways to work with us. They can be combined — most customers start with a pilot inside Launch, then move to Managed.
Launch
Fixed-scope implementation: deploy to your environment or ours, connect BigCommerce, configure roles, rules, thresholds and reports, backfill, set AI policy, train the team, agree go-live criteria and a hypercare window.
Duration: two to six weeks. Output: a live platform, documented, with your team trained. The exit is clean — you own the code, the documentation and the environment.
Managed
We run operations, monitoring, upgrades and support end to end. Three tiers: self-managed with support, advisory retained, or fully managed.
Best for: teams who want the answers without owning the infrastructure.
Build
Entirely new products and platforms from zero — discovery, architecture, UX, engineering, delivery and support. New SaaS, internal platforms, AI assistants and automation tooling.
Best for: a new idea that needs a real product behind it.
Advisory
Architecture review, audits, technology selection and advice, with scheduled reviews and planning. Your team builds; we de-risk it.
Best for: internal teams that need a second opinion and a plan.
Pricing & Engagement Models
We are not only a product. MageTech Solutions is a BigCommerce development, customisation, integration, AI and managed-services partner — so a single fix, one new module and a full multi-year programme are all straightforward to buy. Seven ways to work with us, and every figure is a starting point, because the right price depends on scope, existing systems and integration complexity.
Hourly development
$25/ hourA specific requirement, bug fix, enhancement or small integration where the scope is not yet fixed.
Dedicated resource
$1,500/ monthOne continuous senior resource on your team — part-time or full-time — for ongoing development and support.
Module development
$1,000fromBuild or enhance one defined module or integration. The natural way to adopt the platform piece by piece.
Fixed-price project
$5,000fromA clearly defined deliverable with agreed scope, acceptance criteria and a fixed price.
Maintenance & support
$299/ monthKeep an existing application reliable with monitoring, fixes, small enhancements and technical reviews.
Dedicated team
$6,000/ monthA complete MageTech engineering team — developer, backend, frontend, QA and lead — as an extension of your organisation.
Enterprise & custom engagement
Tell us what you need — we will design the right engagementEnterprise implementations, AI commerce projects, complex or multi-store integrations, ERP and CRM connectivity, data migration, custom BigCommerce applications and long-term product engineering. Scoped individually, with a written proposal before any work begins.
How to read these numbers
Every amount is a starting-from figure, not a fixed price. Final effort depends on your existing systems, the number of integrations, data volume, customisation and how much work is already reusable. We confirm effort, timeline, team and price in a written proposal before starting — we never invoice against an open-ended assumption.
Pricing detail by engagement model
Open any model below for its rate card, inclusions and scope drivers.
For a specific requirement, bug fix, enhancement or small integration. Work is tracked, prioritised with you, and reported against the agreed estimate.
| Service | Recommended rate | Typical work |
|---|---|---|
| BigCommerce development | $25–$40 / hour | Storefront and app changes, catalogue, checkout, theme work |
| AI / commerce intelligence development | $35–$60 / hour | Insight detectors, copilots, recommendation and forecast logic |
| Integration / API development | $30–$50 / hour | BigCommerce, ERP, CRM, webhook and third-party connectivity |
| UI / frontend development | $25–$40 / hour | Design systems, components, data visualisation, accessibility |
| QA / testing | $20–$30 / hour | Functional, regression and browser end-to-end testing |
| Technical consultation | $40–$75 / hour | Architecture review, audits, technology selection, advice |
India-focused engagements: an equivalent starting range of ₹1,500–₹4,500 / hour, depending on skill level and complexity.
For customers who need a developer or technical resource continuously — typically an existing product team that needs additional BigCommerce, AI or integration expertise.
| Dedicated resource | Monthly starting from | Best suited to |
|---|---|---|
| Junior developer | $1,500 / month | Maintenance, small fixes, front-end work under guidance |
| Mid-level developer | $2,500 / month | Feature delivery across the stack, integration work |
| Senior developer | $3,500 / month | Architecture, complex features, code review, technical ownership |
| Senior AI / integration engineer | $4,000+ / month | Data platforms, AI systems, enterprise integrations |
- Part-time
- 80 hours / month
- Full-time
- 160 hours / month
- Reporting
- Direct to your lead, in your repository and ceremonies
- Commitment
- Rolling monthly, with a defined notice period
Why this model exists
Most clients who buy a dedicated resource already have a team and a roadmap; what they lack is specific depth. This adds one accountable senior engineer without a recruitment cycle, and it scales into a full team when the scope grows.
Instead of buying the whole platform, purchase the modules you need. Each is scoped, delivered and priced on its own, and every one runs on the same shared foundation — so nothing has to be rebuilt later.
| Module | Starting from | Module | Starting from |
|---|---|---|---|
| Commerce Dashboard | $1,500+ | AI Insights | $2,500+ |
| Sales Intelligence | $2,000+ | AI Recommendations | $2,500+ |
| Customer Intelligence | $2,000+ | Custom Reports | $1,000+ |
| Product Intelligence | $2,000+ | BigCommerce API Integration | $1,000+ |
| Inventory Intelligence | $2,000+ | External ERP / CRM Integration | $1,500+ |
| Marketing Intelligence | $2,000+ | Custom AI Integration | $2,500+ |
We use "starting from" deliberately: actual effort depends on scope, existing systems and integration complexity. Bundling modules is normally cheaper than buying them separately, and a full platform implementation is priced as a project — see below.
The most common purchase path
Start with the Dashboard and one intelligence module, prove the value on real data, then add the rest as budget allows. Because the data layer is shared from day one, each additional module is cheaper than the first.
For customers who want a clearly defined deliverable. Scope, acceptance criteria, timeline and price are agreed before work starts.
BigCommerce AI Commerce Intelligence — implementation
$5,000starting from- BigCommerce integration and store data synchronisation
- Commerce dashboard with core metrics
- Sales, customer, product and inventory analytics
- AI insights and prioritised recommendations
- Custom reports and exports
- User access, roles and permissions
- Deployment and environment configuration
- Documentation, data dictionary and handover training
Larger enterprise implementations
$10,000–$30,000+Depending on integrations, customisation, AI requirements, data volume and deployment model.
| Scope driver | Typical effect on effort |
|---|---|
| Number of stores | Each additional store adds connection, sync and reconciliation work |
| External integrations | ERP, CRM, marketplace and payment connectivity |
| Data history | Backfill depth and initial sync volume |
| Customisation | White-labelling, bespoke metrics, custom report formats |
| Deployment | Managed cloud, customer infrastructure or hybrid |
| AI requirements | Provider choice, privacy mode, volume and budget |
Also available as projects
Storefront or BigCommerce app development, data migration from spreadsheets or legacy systems, custom reporting programmes, white-label platform builds, and performance or security remediation projects.
Ongoing support for an application we built or one we inherited. Priced monthly, cancellable, with defined response targets.
| Plan | Monthly | Response target | Included |
|---|---|---|---|
| Essential Support | $299 | Next business day | Bug fixes · basic technical support · minor configuration changes · monitoring · monthly maintenance |
| Business Support | $599 | Same business day | Everything in Essential · priority support · small enhancements · API and integration support · performance monitoring · monthly technical review |
| Enterprise Support | $1,499+ | Within hours, SLA-based | Priority and SLA-based support · production monitoring · advanced troubleshooting · integration support · security and technical reviews · continuous improvements · dedicated support contact |
Covered platforms
BigCommerce stores and apps, the MTS intelligence platform, custom web applications, APIs and integrations we built — and inherited applications after a review.
Also available
A managed service where we operate and monitor the platform and you simply use the product; or a support-only arrangement on your own infrastructure.
Not included
Unrequested feature development. That is quoted separately as a module, project or hourly item, so support cost never hides development cost.
For larger customers who want MageTech Solutions to operate as an extended development team rather than a sequence of projects.
A typical team, scaled to the requirement:
- BigCommerce developer — store, catalogue, app and integration work
- Backend / API developer — services, data layer, jobs and integrations
- Frontend developer — application, dashboards, design system, charts
- QA engineer — functional, regression and end-to-end automation
- Technical lead / project manager — architecture, prioritisation, communication and quality
Composition, seniority and hours are agreed per engagement. A single dedicated resource is available at a lower entry point — see model 02.
What the team operates under
| Aspect | How it works |
|---|---|
| Delivery | Short iterations against a prioritised backlog you influence |
| Communication | Weekly demonstration, written decisions, one escalation path |
| Quality | The same pipeline standard applies to every change we make in your account |
| Documentation | Updated in the same change, not at the end of the engagement |
| Ownership | Your repository, your infrastructure, your cloud — we work in your estate |
| Exit | Source, documentation and configuration are yours throughout |
Common pairing
Most teams start with BigCommerce, backend and QA capacity, add a dedicated resource from model 02, and take AI and data engineering on demand as the intelligence layer grows.
How to choose — the hybrid model
We do not price only by hours, because that positions us as a staffing supplier rather than a technology partner. The right model follows the shape of the requirement.
Hourly
A fix, an enhancement, a question, something undefined. Model 01 from $25/hour.
Module or fixed price
A defined module, integration or report with a known outcome. Models 03 and 04 from $1,000 and $5,000.
Project price
A full implementation with scope, milestones and acceptance criteria. Model 04 from $5,000, typically $10,000–$30,000+.
Dedicated resource or team
An ongoing roadmap needing steady capacity. Model 02 from $1,500/month or model 06 from $6,000/month.
Monthly maintenance
Something already works and must keep working. Model 05 from $299/month.
Custom engagement
Multi-store, ERP and CRM, migration, AI programmes, long-term product engineering. Model 07, scoped individually.
The commercial principle
We recommend the model that fits the requirement, not the one that maximises revenue. A client who starts with a $1,500 module and a good experience comes back for the platform; a client over-committed to a large fixed scope does not.
How we work
- Step 01RequirementYou share the requirement, the constraint and the outcome you need. A conversation, a document, a call recording — whatever is easiest.
- Step 02DiscoveryWe review the application, the APIs, the workflows and the technical scope. Where we need to see a live store or codebase, this is where access is agreed.
- Step 03ProposalEstimated effort, timeline, team and pricing — in writing, with what is included and what is explicitly out of scope.
- Step 04DevelopmentOur team develops, tests and reviews in short cycles, with visible progress rather than status reports.
- Step 05DeliveryDeployment, documentation, training and handover. Source code and knowledge transfer are part of delivery, not an extra.
- Step 06SupportMaintenance, enhancements and continuous improvement — on whichever model suits you, including none.
Ask for a customised proposal
Enterprise implementations, AI commerce projects, complex or multi-store integrations, ERP and CRM connectivity, custom BigCommerce applications, data migration and long-term product engineering all start with the same sentence: tell us what you need, and we will design the right engagement model.
We publish our prices because we would rather compete on the work than on the opacity of the quote.
Support & Maintenance
The product does not end at go-live. This is the loop we commit to.
Monitor
App, database, queues and jobs watched continuouslyRespond
Triage by severity, named contact, agreed responseFix
Ships through the same CI gates as featuresUpgrade
Dependencies and platform APIs, tested in staging firstReview
Quarterly reliability, adoption, data and roadmapAlways on
Security patches, API change handling, verified backupsEvery engagement has a documented escalation path with named contacts, a severity definition, and a response commitment agreed in writing before work starts. What we need from you: a named business owner for decisions and acceptance, timely access to the store and third-party systems, one weekly decision window, and feedback inside agreed review checkpoints.
Quality, Security & Compliance
The summary you can hand to your procurement or security review.
| Control | Implementation in this platform | Status |
|---|---|---|
| Password storage | bcrypt hashing; plaintext never stored or logged | Live |
| Session management | Opaque token in an HttpOnly, SameSite cookie; only a hash stored | Live |
| Integration secrets | AES-256-GCM encryption at rest with a supplied key | Live |
| Authorisation | Permission guard on every sensitive endpoint; 51 enforced permissions | Live |
| Tenant isolation | Session-derived tenant scope injected at the database client | Live |
| Audit trail | Actor, action, entity, metadata and IP for administrative changes | Live |
| Webhook verification | Raw-body HMAC verification with event-hash idempotency | Live |
| Data integrity | Item prices snapshotted at order time; idempotent upserts keyed on external identifiers | Live |
| Rate limiting & security headers | Hardening phase, scheduled before internet exposure | Phase 2 |
| OAuth state and CSRF token | Added with the PKCE and session-rotation work | Phase 2 |
| Password reset, MFA, email verification | Account lifecycle phase | Phase 2 |
Our promise on claims: every number we show you in a demo comes from the running product. Every capability we describe is either built, or labelled as roadmap. If it is not in the system, we will say so. Known limitations are documented in our own materials — not discovered in month three.
Outcomes We Target
We agree measurable success criteria before the pilot and report against them afterwards.
| Area | What success looks like | How we measure it |
|---|---|---|
| Time to insight | Minutes from question to answer, instead of a manual report cycle | Adoption and response-time feedback from the weekly user |
| Stock & service levels | Fewer avoidable stockouts on priority SKUs | Coverage alerts raised versus stockout events |
| Customer retention | At-risk customers identified before they lapse | Win-back campaign response from the at-risk segment |
| Marketing efficiency | Spend shifted toward campaigns that actually return revenue | ROAS movement on reallocated spend |
| Reporting effort | Finance cycles rebuilt by hand, replaced by exports | Hours spent on manual report preparation |
| Trust & governance | Every number traceable, every action audited, every role scoped | Reconciliation success, audit completeness, access reviews |
| Delivery certainty | Working version early, no surprises at handover | Milestone adherence against the agreed plan |
Demo Readiness Checklist
Environment
- Seeded demo workspace present and current
- Web, API and worker running; no errors in the logs
- Insight generation and alert sweep completed
- At least one generated report available to download
- Sync history shows a completed run
- Health endpoint green and database reachable
Presentation
- Walk the demo script in order
- Two roles ready to demonstrate access control
- Copilot questions prepared for the customer's industry
- Known limits stated before they are discovered
- Documents shared in advance
- Leave with a decision and a date
Commercial
- Know the store count, channels and team size
- Understand their data questions beforehand
- Written pilot scope and timeline ready to send
- Know which engagement model you recommend, and why
- Agree the success criteria you will be measured on
Quick answers if you are asked live. "Is the AI accurate?" — findings come from deterministic rules on your own data; models only explain them. "Does our data leave?" — not by default, the AI layer runs locally unless you enable a provider. "Do we own it?" — code, documentation, infrastructure definition and CI, in full. "How soon?" — a pilot on your data in one to two weeks; production in three to six. "Who maintains it?" — you, with support, or we do; both on the same runbook.
Next Steps
1 · A live demo
Thirty minutes on a seeded workspace, with your questions answered live. You keep access afterwards to explore on your own. Best for: confirming the product is real.
2 · A pilot on your data
Time-boxed, with your store connected, your numbers verified, your team trained, and written success criteria agreed up front. Best for: proving value with your own figures.
3 · A delivery or managed service
Full implementation, custom development, or a managed service where we run the platform and you run your business. Best for: committing to the platform.
What we need from you to start: a store hash and access token, or an OAuth app installation — the fastest way to show real data. The two or three questions your team most wants answered. Who will use the platform, and in which roles. Your decision date, so we can plan a pilot properly.
Appendix — Quick Reference
| Item | Detail |
|---|---|
| Modules | Dashboard, Sales, Customers, Products, Inventory, Marketing, Insights, Reports, Alerts, Settings |
| API | 64 endpoints, 18 controllers, session and permission guarded |
| Data | PostgreSQL 16 with 32 models and 30 enums, tenant-scoped |
| Jobs | 5 queues, 5 schedules, retries with exponential backoff |
| BigCommerce | OAuth 2.0, API-account token, signed webhooks, 7 sync entities |
| AI | Rule engine with 7 detectors, optional LLM copilot, 3 privacy modes |
| Exports | 4 report types, CSV, tenant-partitioned, schedulable |
| Quality | Strict types, lint, 10 end-to-end tests, CI on every push |
| Access | 7 roles, 25 permissions, tenant isolation, audit log |
| Deployment | Docker-ready containers, environment-driven configuration |
The one-line difference: most vendors sell a dashboard, a report, or a model. We hand over a working commerce platform, the engineering capability to extend it indefinitely, and a delivery model where a working version is in your hands in weeks — with the code and the knowledge to make it yours.
We do not ask you to trust a slide deck. We ask you for thirty minutes with a working system.
A working platform today. The team to extend it tomorrow.
Ten modules. Explainable AI. A real BigCommerce integration. A delivery model built for weeks rather than quarters.
🌐 www.magetechsol.com · 📞 +91 99442 55515 · 📞 +91 96637 21018
| Product Name | MTS BigCommerce AI Commerce Intelligence |
| Product Category | BigCommerce Apps & Integrations |
| Product Type | Multi-Tenant Commerce Intelligence & Decision Platform |
| Built By | MageTech Solutions |
| Version | 1.0 |
| Modules | Dashboard · Sales · Customers · Products · Inventory · Marketing · Insights · Reports · Alerts · Settings |
| Product Modules | 10 |
| REST Endpoints | 64 (18 controllers, session and permission guarded) |
| Data Models | 32 models · 30 enums (PostgreSQL 16, tenant-scoped) |
| Access Roles | 7 roles · 25 permissions · 51 enforced endpoint permissions |
| Queues & Schedules | 5 BullMQ queues · 5 repeatable schedules · retries with exponential backoff |
| Automated E2E Tests | 10 browser tests, CI on every push |
| Frontend | Next.js 15 (App Router) · React 19 · Tailwind CSS 4 · TypeScript 5 (strict) · SVG data visualisation |
| Backend | NestJS 11 · REST API design · guards and interceptors · Zod validation |
| Database | PostgreSQL 16 with Prisma 6 ORM, migrations, indexing and pre-aggregated snapshots |
| Cache & Queues | Redis 7 with BullMQ 5 and repeatable schedulers |
| Monorepo | Turborepo monorepo with shared packages for environment, crypto, metrics and integrations |
| AI Engine | Deterministic rule engine with 7 detectors · optional LLM copilot (OpenAI / Anthropic / Gemini adapters) · 3 privacy modes · per-tenant usage metering |
| BigCommerce Integration | OAuth 2.0 · API-account token · V2/V3 API clients · signed webhooks with raw-body HMAC verification · 7 sync entities |
| Sync Entities | Categories · Brands · Products · Customers · Orders · Inventory · Store settings |
| Background Jobs | Dispatch syncs · nightly full sync · daily insights · alert sweeps · report delivery |
| Exports | 4 report types (sales, inventory, customer, full dataset) · CSV · tenant-partitioned · schedulable |
| Security | bcrypt passwords · opaque hashed session tokens in HttpOnly SameSite cookies · AES-256-GCM integration secrets · server-side permission guards · tenant-scoped queries · HMAC-verified webhooks · full audit trail |
| Privacy | Local-first AI with explicit privacy modes; no merchant data leaves your infrastructure unless you enable a provider |
| Deployment | Docker-ready containers for web, API, worker, database and queue · environment-driven configuration · documented runbook and rollback procedure |
| CI / CD | GitHub Actions — build, lint, strict type gate and browser end-to-end suite on every push |
| Design System | Token architecture, component library, brand theming, per-module accent colours, accessible interaction patterns |
| Demo Environment | Seeded tenant with 1,284 products · 12,450 customers · 8,420 orders · 21,160 order lines · 366 daily snapshots · $284.5K revenue · 15 campaigns · 6 segments · 7 insights · 5 alert rules |
| Handover | Source code · database schema and migrations · CI pipeline · technical architecture, API and data model documentation · development and deployment guide · administrator guide · role-based training · recorded walkthroughs |
| Engagement Models | Launch (fixed-scope) · Managed (self-managed / advisory / fully managed) · Build (from scratch) · Advisory |
| Typical Timeline | Pilot on your data in 1–2 weeks · Production in 3–6 weeks · 90-day hypercare · Quarterly outcome reviews |
| Support Options | Self-managed with support · Advisory retained · Fully managed — with agreed response terms and a documented escalation path |
| Pricing Model | Custom / scoped — engagement models from hourly development ($25/hr) and module development ($1,000 from) to fixed-price projects ($5,000 from), dedicated resources ($1,500/month) and dedicated teams ($6,000/month) |
| Status | Live — reference implementation, running and demoable today |
| License | Full source handover — you own the code, documentation, infrastructure definition and CI |
| Website | www.magetechsol.com |
| Contact | +91 99442 55515 · +91 96637 21018 |
| Company | MageTech Solutions |
BigCommerce AI Commerce Intelligence — Demo Document
View the complete product documentation below.
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