AI Strategy Consulting
AI Strategy & Roadmap
Decide what AI to build (and not build) before you spend the budget.
A focused engagement for product teams considering AI. We pressure-test the user problem, evaluate data readiness, model the cost and latency at scale, and map a sequence of features ranked by feasibility and impact. You leave with a written roadmap your engineering team can act on.
What you get
- Free 30-minute strategy call (no card, no follow-up sequence)
- Written 48-hour AI Integration Audit on request
- Prioritised feature list with build/buy/skip recommendations
- Cost-at-scale model for the recommended AI stack
AI/ML Engineering
Machine Learning, RAG & LLM Systems
Production RAG, semantic search, multi-LLM routing, and AI agents that are eval'd, observable, and cost-controlled.
We build the AI systems behind real products. RAG over millions of documents (DecoverAI: legal discovery on Pinecone + AWS EKS, helped raise $2M+ seed). Multi-LLM routers that pick the right model per task. Agent systems with proper evals, observability, and cost ceilings. Every system ships with monitoring, regression suites, and a runbook your team owns.
What you get
- RAG pipeline (vector DB, retrieval, re-ranking, eval harness)
- Multi-LLM routing with cost and latency budgets
- Production observability (Langfuse, OpenTelemetry, custom dashboards)
- Hallucination guardrails and validation layers
- Documentation and team handoff
SaaS Product Engineering
Full-Stack SaaS Development
Production SaaS infrastructure on Next.js, Node, Python, MongoDB/Postgres, and AWS/GCP, built to scale.
We build the full product around the AI: auth, billing, dashboards, multi-tenancy, admin tooling, and the surrounding workflows. 50+ projects shipped across SaaS, marketplaces, and internal tools, with 1M+ cumulative users in production.
What you get
- Frontend (Next.js / React) and backend (Node / Python)
- Database design (Postgres, MongoDB) with migrations
- Auth, billing, multi-tenancy, RBAC
- CI/CD on AWS or GCP with environment isolation
- Performance budgets and Core Web Vitals targets
Data Engineering
Data Engineering & Pipelines
ETL/ELT, vector pipelines, and the data plumbing AI features need to actually work.
Most AI features fail not because of the model but because of the data feeding it. We build the pipelines: ingestion, normalisation, embedding generation, vector storage, refresh schedules, and quality monitors. Designed for ongoing maintenance, not one-shot demos.
What you get
- Ingestion pipelines (batch and streaming)
- Embedding generation and vector store population
- Data quality monitors and drift detection
- Backfills, replays, and schema evolution support
Not sure which one fits?
Start with the strategy call. 30 minutes, free, no follow-up sequence. You leave with a clear read on whether AI is the right move and what to do first.