Services
Three pillars.
Nine engagements.
Strategy, implementation, and infrastructure for funded teams shipping AI. Every engagement is fixed-scope with named deliverables and measurable outcomes.
Pillar 01
Strategic Guidance
Navigate AI complexity with fractional engineering leadership. Roadmaps optimized for ROI and a path to scale.
Pillar 02
AI Implementation
From copilots to fine-tuned models — production builds your engineering team owns.
Pillar 03
AI Infrastructure
Production-grade cloud infrastructure for AI/ML — optimized for cost, control, and scale.
Live demo reel
What we actually ship.
Every engagement leaves behind something you can open: a copilot, a retrieval index, a CI pipeline, a dashboard. Hover a tab to preview the artifact — click for the full engagement.
How we work
Five steps. No surprises.
Every engagement runs the same playbook — so you know exactly what happens before you sign, and exactly what "done" looks like.
- 01
Discovery
Free 20-minute intro call. We get specific about the problem, your success metrics, and your timeline — and tell you on the spot if we're not the right fit.
- 02
Diagnostic
Paid 60–90 minute working session against your live system. 12-point assessment delivered as a written 1-page diagnostic plus session recording within 48 hours.
- 03
Build
Fixed-scope Statement of Work with named deliverables and a single price. Senior engineers ship in your repo, your cloud, against your eval harness. Weekly demos, not status decks.
- 04
Ship
Production handoff: runbooks, golden eval sets, on-call rotation guide, and the rollback paths your team will actually use at 2am. Code lives in your repos from day one.
- 05
Metrics
Every engagement ships against 3–5 measurable outcomes — MRR, p95 latency, eval pass rate, cost-per-query — agreed in writing before we start. We don't bill the last invoice until they're hit.
Pillar 01
Strategic Guidance
Navigate AI complexity with fractional engineering leadership. Roadmaps optimized for ROI and a path to scale.
01 · 2–4 weeks
AI Strategy
Fractional CTO expertise and roadmaps optimized for ROI and scale.
View full details- Founder or CTO building an AI thesis with real budget behind it
- Need an outside read on build-vs-buy and sequencing
- Will translate findings into board-level decisions
- AI roadmap with phased delivery and budget bands
- Build-vs-buy analysis per surface
- Hiring profile for the first 2 AI engineers
- Risk register with mitigation plans
A defensible 12-month AI roadmap your CEO and board can fund — with sequencing that respects what your team can actually ship.
Pillar 02
AI Implementation
From copilots to fine-tuned models — production builds your engineering team owns.
02 · 6–10 weeks
AI Copilots
In-product assistants grounded in customer data, with streaming, citations, and refusal logic.
View full details- Live SaaS product with rich customer data
- Need an in-product chat or assistant surface
- Team that owns the codebase after handoff
- Streaming chat surface with tool-use orchestration
- Citation-grounded answer schemas
- Per-tenant retrieval scoping and isolation
- Refusal precision tuning + golden eval set
- Cost-per-conversation budgeting and dashboards
A copilot your customers actually use — with measurable wins on grounding, citation integrity, and cost per conversation.
03 · 4–8 weeks
Document Processing
Process thousands of structured and unstructured documents — complex layouts, tables, and forms.
View full details- High-volume document workflows blocking ops or compliance
- Mixed inputs: PDFs, scans, semi-structured forms
- A reviewer-in-the-loop step you want to keep auditable
- Document classifier and extraction pipeline
- Reviewer queue with confidence-based routing
- Schema validators and human-handoff hooks
- Throughput + accuracy dashboards
An auditable extraction pipeline with named accuracy on your document mix — and a reviewer surface your ops team trusts.
04 · 3–5 weeks
RAG & Embedding
Chunking, hybrid retrieval, reranking, and citation systems — measured against your golden set before they ship.
View full details- Live RAG system shipped to paying customers
- Willingness to instrument production with our eval harness
- Single decision-maker on your side
- Chunking redesign with three strategies benchmarked
- Hybrid BM25 + dense retrieval with reranker
- Citation system with source-grounding checks
- Golden eval dataset of 500+ queries with LLM-as-judge scoring
- CI gates so regressions block deploys
Named before/after metrics on hallucination rate, retrieval precision @5, P95 latency, and cost-per-query — methodology your team can re-run forever.
05 · 4–8 weeks
Fine-Tuning & Inference
Specialized models that reduce cost and improve accuracy for your domain.
View full details- A bounded task where general models cost or underperform
- Access to representative labeled data (or a path to it)
- Appetite for an eval harness to measure regressions
- Dataset curation and labeling rubric
- Fine-tune across two base models, benchmarked
- Inference deployment with autoscaling
- Eval suite + drift monitoring
A specialised model that beats your current general-purpose baseline on accuracy and unit cost — with a path to keep it that way.
06 · 6–12 weeks
AI Agents
Multi-step agents that call your tools, your APIs, and your data — with deterministic rollback paths.
View full details- Workflow with clear tools, APIs, and side effects to orchestrate
- Engineering org ready for trace + replay infra
- Guardrails treated as first-class, not an afterthought
- Tool + API orchestration with typed schemas
- Step-level trace + replay infrastructure
- Per-step eval coverage, not just end-to-end
- Rollback paths for every side-effect tool
- Handoff package: runbooks, eval suite, rotation guide
An agent that finishes the job — with traces you can replay, guardrails that fire when they should, and rollback when they don't.
Pillar 03
AI Infrastructure
Production-grade cloud infrastructure for AI/ML — optimized for cost, control, and scale.
07 · 4–8 weeks
Cloud Migration
Migrate from third-party model APIs to your own cloud — cut costs while gaining control and flexibility.
View full details- Spend on third-party model APIs is material and growing
- Need for data residency or VPC isolation
- Engineering org that can own the resulting stack
- Cost + capability comparison across providers
- Cutover plan with shadow-traffic validation
- Inference service deployed in your account
- Unit-economics dashboard tracking pre/post cost
Inference running in your cloud, under your control, with a named monthly cost reduction and a documented rollback path.
08 · 3–6 weeks
DevOps & Infrastructure
Auto-scaling, production-ready cloud infrastructure tuned for AI/ML workloads.
View full details- AI workloads outgrowing a hand-rolled deployment
- Need CI/CD that respects prompts, retrieval, and models as artifacts
- On-call rotation that wants better signal
- IaC for inference + retrieval services in your cloud
- CI/CD with prompt + retrieval versioning
- Autoscaling tuned to your latency and cost targets
- Secrets, network, and access posture review
Production infra your team can extend, scale, and on-call against — with deploy times measured in minutes, not days.
09 · 3–4 weeks
Production Telemetry
P95 latency, cost-per-query, drift, and eval regressions — on dashboards your on-call actually opens.
View full details- AI feature already in production with real traffic
- On-call rotation that needs better signal
- Existing observability stack we can extend
- Latency + cost dashboards per tenant
- Embedding drift detection + alerting
- Eval regression alerts on every deploy
- Incident playbooks + rollback paths
- On-call runbook tailored to your stack
When your AI gets worse, your team knows in minutes — not quarters. Signal your on-call rotation actually trusts.
FAQ
Real questions, technically answered.
- How fast can you start?
- Most engagements kick off within 1–2 weeks of a signed Statement of Work. The discovery call is usually the same week you ask.
- Do you work fixed-scope or time-and-materials?
- Default is fixed scope with named deliverables and a fixed timeline. We will do retainer or T&M for incident-response or ongoing reliability work, never for greenfield builds.
- Can we use our own LLM provider and vector DB?
- Yes. We are model- and infrastructure-agnostic. Shipped against OpenAI, Anthropic, Bedrock, Vertex, self-hosted Llama, and against Pinecone, Weaviate, pgvector, Turbopuffer, and Mongo Atlas Vector.
- What happens after delivery — retainers, handoff, training?
- Engagements end with a written handoff package: runbooks, eval suite, on-call rotation guide. Optional monthly retainer for incident review and roadmap input. No multi-year MSAs.
- Do you sign NDAs and BAAs?
- Yes to both. NDAs before any production data changes hands. BAAs for HIPAA-scoped engagements.
Next step
Not sure which engagement fits?
Book a discovery call. We'll map your situation against the pillars above and tell you — honestly — whether we're the right partner.
Refundable if we're not a fitWritten diagnostic in 48 hoursSession run by a founder, not a sales rep