When off-the-shelf AI
does not fit.
Most AI projects fail at deployment, not at the demo.
Three notebooks, a OpenAI key in a Slack message and a working PoC. Six months later it has not made it to production and the budget is gone.
You shipped an AI feature. Is it actually working? You have no eval suite, no regressions catch, no way to compare model versions.
AI in production needs caching, retries, circuit breakers, model routing, cost guardrails, security review. Most teams ship it like a demo.
The full lifecycle, owned by us.
We sit down with the business owner, identify what AI should and should not do, write the success criteria. No "let's try GPT" projects.
Multi-agent or single-agent, role definitions, tool sets, escalation rules, human-in-the-loop boundaries. Designed before written.
Source ingestion, chunking strategy, embedding model selection, hybrid retrieval, re-ranking, citation enforcement. Tuned for your data.
Build a real evaluation suite first, then fine-tune (LoRA / full / DPO) only if data shows it improves the suite. No vibes-based training.
Cheap models for triage, premium for hard tasks, all behind one proxy with per-project cost guardrails and fallbacks.
Real eval datasets per use case (golden Q&A, edge cases, hallucination tests, regression suites) running in CI on every change.
CRM, helpdesk, accounting, drives, custom APIs — exposed as agent tools with retries, idempotency, and audit logs.
Every prompt, retrieval, tool call and response logged. Streamable to your SIEM. Searchable by case, by user, by outcome.
After launch we keep running it: prompt updates, model upgrades, eval rerun, cost reports, escalation handling. Managed AI service.
The custom-AI projects we keep getting asked for.
Invoice OCR + classification + ERP push, contract clause extraction, automated KYC document review, claim processing.
Real-time sentiment on inbound calls / messages with escalation rules, intent classification per business line, churn risk prediction.
"When this ticket comes in, classify, draft a response, route to the right team, summarise for the manager weekly." End-to-end agent flows.
A clinic-specific Oracle-class agent with medical guardrails, a legal assistant trained on your firm's precedent library, an HR bot for your policy book.
You have a product. You want AI inside it. We build and operate the AI layer; you keep the product brand and customer relationship.
Three packages, sized to your scale.
Every quote is tailored. Tell us your setup, we come back with a fixed number within one business day.
Two-week scoping engagement with a designed plan + cost estimate.
Best fit: "we want AI but do not know what to build".
- Stakeholder workshops
- Use-case prioritisation matrix
- Architecture proposal + tech choices
- Eval criteria + success metrics
- Cost estimate for Build phase
- Two-week timeline
Custom AI build to production, including evals + integrations.
Best fit: scoped use case ready to ship in 6–12 weeks.
- Custom agent / RAG / pipeline build
- Eval suite + CI integration
- Production wiring (auth, retries, observability)
- Cost guardrails + model routing
- Documentation + runbook
- First 30 days of post-launch tuning
Ongoing managed AI service with monthly tuning + reports.
Best fit: production AI you do not want to operate yourself.
- Continuous prompt + model tuning
- Monthly eval reruns + drift detection
- Cost report + optimisation
- Quarterly model upgrades
- Incident response + on-call
- Dedicated AI engineer
- 24/7 critical SLA
Done-for-you, in weeks.
- Week 1–2Discovery
Workshops, use-case scoping, architecture, eval criteria, cost estimate, build plan.
- Week 3–6Build
Implement the agent / pipeline, build evals, integrate with your systems, instrument observability.
- Week 7–8Eval + harden
Run evals, tune until pass thresholds met, security review, load test, cost analysis.
- Week 9+Production + operate
Deploy to production behind feature flags, ramp traffic, monitor, hand into Operate tier (if scoped).