Services
Small, senior pods that build AI features and the infrastructure to run them. Work happens in your cloud; delivery includes code, dashboards, and runbooks.
Owner on our side
one accountable lead
Cadence
weekly demo + notes (done / next / risks)
Deployment
client VPC/VNet, least-privilege IAM, secrets management
Billing
milestone-based for pilots; monthly for retainers
Delivery Pods
Purpose: ship user-facing AI features (LLM UX, recommendations, in-app assistant, intelligent search).
Use when: you want a production pilot in 8–12 weeks tied to a concrete KPI.
Deliverables
- Product spec for AI (flows, prompts/system messages, guardrails, analytics)
- Data preparation (ingestion/joins/quality checks)
- Models or RAG pipeline (embeddings, retriever, re-ranker)
- APIs/SDKs and optional UI hooks
- Evaluation harness (golden sets, offline/online metrics)
- Observability (latency/quality/cost dashboards)
- Runbooks and handover checklist
Team: AI product lead · LLM engineer · MLOps engineer (designer as needed)
Acceptance: KPI + technical criteria agreed up front; end-of-pilot demo against them
Typical risks addressed: latency budgets, hallucinations (grounding/refusals), cold-start, catalog quality, change control
Engagement & Pricing (summary)
1
Quick Win
4-6 weeks
One use-case demo/POC; narrow data; KPI hypothesis; code and docs handed over.
2
Pilot Build
8–12 weeks
Production MVP: data → model/RAG → API/UI → eval/monitoring. Milestone billing. Acceptance criteria defined at kickoff.
3
Retainer
(3–12 months)
Ongoing capacity to extend features, improve metrics, and operate to SLOs. Monthly report on latency/quality/cost and planned work.
Ownership: you own custom code, configs, and IaC in your repos.