AI platform design & implementation
Your AI platform,
production-grade from day one.
Most enterprise AI dies between the prototype and production. We close that gap: a complete platform — data pipelines, retrieval, model serving, isolation, monitoring and an audit trail — configured to your business and deployed into infrastructure you own. Built on the same architecture that runs our own AI platform in production.
Proven in production
The reference architecture runs our own platform: agentic extraction across 80+ markets, vector search, daily AI matching.
Adapted, not reinvented
Pre-validated decisions on retrieval, evaluation, serving and security — configured to your context instead of designed from scratch.
Compliance as architecture
EU AI Act-aligned controls — documentation, human oversight, tamper-evident logs — are platform capabilities, not paperwork.
You own everything
Open standards, your cloud, your repositories, your keys. No licence fees, no vendor lock-in — including to us.
Reference architecture
Seven layers, one coherent platform
Every engagement starts from the same layered blueprint and strips it to what your use cases actually need. Each layer ships with pre-made decisions — so weeks normally spent on architecture debates go into delivery instead.
Channels
Web, mobile, partner APIs and AI-agent interfaces — anything a human can do through the UI, an agent can do through the same action model.
Experience
Backend-for-frontend services, customer identity, sessions and feature flags — fast edges in front of the domain core.
Domain services
Your business logic as bounded contexts with contract-first APIs, each publishing data products with explicit schemas and quality checks.
Integration
API gateway for synchronous calls, an event backbone for everything else, and durable workflows for long-running processes.
Data & AI
Ingestion pipelines, warehouse/lakehouse layers, vector store and embedding pipeline, feature data — retrieval decoupled from generation.
Platform
Cloud landing zones, identity and access, container runtime, CI/CD with security gates — everything as code, no console-driven changes.
Cross-cutting
Security, observability, and an append-only evidence store: every decision, deployment and data access leaves a queryable audit record.
Default stack
Chosen to be boring where boring is a virtue — and hireable, so your team can run it.
- APIs & services
- NestJS on Node.js (TypeScript)
- Frontends
- Next.js + React
- Data & vectors
- PostgreSQL + pgvector
- Pipelines
- Queue-based workers, event-driven
- LLM access
- Multi-provider gateway, cost caps
- Retrieval
- Hybrid search: vector + lexical, reranked
- ML where needed
- Python (training, embeddings)
- Infrastructure
- IaC, containerised, cloud-agnostic
What “production-grade” means here
- Multi-tenant isolation enforced in every store and query path
- Evaluation harness: model and prompt changes gated by regression tests
- Kill switches: every AI decision point has a governed fallback
- Full replayability — any historical AI decision can be reconstructed
- Per-tenant cost metering and token budgets on all AI features
- PII redaction and consent-aware data access where personal data is involved
Delivery methodology
Five phases, explicit gates, no surprises
Each phase ends with a decision gate you control — scope, design, pre-deployment, release, acceptance. Nothing progresses on momentum alone, and every gate leaves an evidence record.
Discover
Data, systems and regulatory context assessed; use cases scored; fixed-price plan produced. 1–2 weeks.
Design
Reference architecture configured to your constraints: data pipelines, retrieval, serving, isolation, controls.
Build
Two-week increments with demos in your sandbox. Contract-first APIs, tests, security scanning on every change.
Deploy
Progressive rollout — staging, canary, production — with automated rollback and your operations team shadowing.
Transition
Training, runbooks, and a shadow period. Done when your team resolves incidents with us only watching.
Proof
The platform we'd build for you is the one we run
Our procurement-intelligence product is built on exactly this architecture — disclosed here the same way as our extraction case studies: it's our own platform, not a client engagement, and it runs live against adversarial real-world sources every day.
80+
procurement markets monitored by agentic extraction pipelines
5-step
extraction ladder — APIs, learned selectors, rules, AI reasoning, parsers
1024-dim
vector embeddings powering semantic search and daily AI matching
Minutes
from source publication to enriched, searchable record via event-driven revalidation
Engagement structure
Scoped, fixed-fee, outcome-aligned
No open-ended time-and-materials. Every build is priced against a written delivery spec with acceptance criteria you sign off before work starts.
Discovery sprint
The entry point for every platform engagement.
1–2 weeks · fixed fee
- Data & infrastructure readiness assessment
- Use-case portfolio scored on impact and feasibility
- Target architecture and delivery plan
- Fixed price and timeline for the build
- Yours to keep — build with us or without us
Platform build
Foundation plus your first production use case.
8–14 weeks typical · fixed fee
- Platform foundation deployed to your cloud
- First use case live with monitoring & governance
- Infrastructure as code, tests, documentation
- Compliance evidence trail from day one
- Demo checkpoints every two weeks
Operate & extend
After handover, if and while you want us.
monthly retainer
- Monitoring, drift alerts and model updates
- New use cases at reduced delivery cost
- Regulatory change tracking (EU AI Act & beyond)
- Quarterly platform health & roadmap review
- Cancel anytime — you own the platform
FAQ
Common questions
- What exactly do we get at the end?
- A running platform in your cloud (or on-premises) environment: infrastructure as code, data pipelines, retrieval and model-serving services, dashboards, tests, runbooks and an audit trail — plus a trained team. Everything is built on open standards and you own all of it. There is no licence fee and no lock-in to us.
- Why is this different from hiring a generic dev agency?
- Two reasons. First, we don’t design from a blank page — we adapt a reference architecture that already runs in production for our own platform, so the security, retrieval, evaluation and governance decisions are pre-made and battle-tested. Second, compliance is built into the architecture (documentation, human oversight, logging), which matters from August 2026 when the EU AI Act’s high-risk obligations become enforceable.
- What stack do you build on?
- TypeScript end-to-end: NestJS for APIs and domain services, Next.js/React for frontends, PostgreSQL with pgvector for data and vector search, queue-based pipelines for ingestion and background work, and a multi-provider LLM gateway. Python is used only where its ML ecosystem is irreplaceable. If you have existing investments we adapt — the architecture is a starting point, not a mandate.
- Can you also size and set up the infrastructure?
- Yes. Platform delivery includes infrastructure sizing — inference capacity, GPU requirements where models are self-hosted, storage and network topology — and deployment via infrastructure-as-code to your cloud accounts. Most engagements use managed cloud AI services plus right-sized compute rather than dedicated GPU clusters, which keeps cost honest.
- How long does it take and what does it cost?
- A first production use case on a new platform foundation typically lands in 8–14 weeks. Every engagement starts with a discovery sprint (1–2 weeks) that produces a scoped, fixed-price delivery plan — so you know the cost, timeline and acceptance criteria before committing to the build.
- What happens after go-live?
- A structured handover: training for your engineers, operational runbooks, and a shadow period where we watch your team run it. After that, most clients keep a light-touch retainer for monitoring, model updates and regulatory change — but the explicit goal is that you can operate and extend the platform without us.
Start with a discovery sprint
Tell us what you're trying to build and what data you have. We'll reply with whether a sprint makes sense, what it would cover, and a fixed price — usually the same day.