Four phases. No theatre.
Every engagement — Audit, Integration, Transformation — runs through the same four phases. Durations change. Depth changes. The shape doesn't. We keep the shape because it's what turns AI consulting into actually-shipping AI.
Production or bust
We ship things that run. Not POCs that live in a demo folder and die there.
Observable by default
If it's not measured, it doesn't exist. Every system ships with metrics, traces, and cost visibility from day one.
Your stack, your code
No proprietary black boxes. Code in your repo, models in your cloud, data in your databases.
Reversible changes
Every deployment is rollbackable. Every data change is audited. Every decision can be undone.
Handover, not hook
The goal is for your team to own what we built. Retainers are a choice, not a trap.
Before we build, we decide what "done" looks like. We pin the business outcome, the success metric, the constraints, and the non-goals. The frame is what protects the engagement from scope creep and the team from vanity metrics.
What we do
- Stakeholder interviews (4–8)
- Process walkthroughs
- Data inventory & samples
- Constraint surfacing (legal, cost, SLA)
- Success metric agreement
Artifacts you get
- Problem statement (1 page)
- Success metric + threshold
- Non-goals list
- Risk register (signed)
- Decision log initialised
Typical tools
- Miro / FigJam for mapping
- Notion / Confluence for docs
- Slack or Teams channel
- Loom walkthroughs
- Shared decision log
A narrow, working proof against real data. Not a slide deck, not a demo. A system that produces a measurable result on your constraints. If it won't clear the success threshold in this phase, we don't ship it — we pivot or stop.
What we do
- End-to-end thin slice
- Model & architecture selection
- Eval harness v1
- Cost & latency benchmark
- Go / no-go checkpoint
Artifacts you get
- Working prototype
- Eval report with hard numbers
- Architecture decision record
- Cost projection
- Risk register v2
Typical tools
- Python · Jupyter
- LangGraph · LlamaIndex
- MLflow · Weights & Biases
- Docker · local clouds
- Postgres · pgvector
The prototype becomes a system. Hardening, guardrails, observability, security, SRE. This is the phase most vendors skip — which is why most AI projects "fail in production." We don't consider it done until it's running with monitoring green in your environment.
What we do
- Production architecture
- Guardrails & safety tests
- Observability stack
- CI/CD & deployment
- Security review & pen-test
Artifacts you get
- Production system live
- Runbooks for ops team
- Incident playbook
- Dashboards (cost, quality, latency)
- Data-flow & compliance docs
Typical tools
- Temporal / LangGraph
- Grafana · OpenTelemetry
- Sentry · Honeycomb
- GitHub Actions · Terraform
- Vault / AWS SM
Handover, training, and the feedback loop that keeps the system honest over time. Drift. Retraining. FinOps. Incident response. This is where the difference between a one-off project and a long-lived capability shows up.
What we do
- Team training (live + recorded)
- Code walkthroughs
- Post-launch office hours
- Drift & cost monitoring
- Quarterly health review
Artifacts you get
- Onboarding docs for new engineers
- Update playbook (model / prompt)
- FinOps dashboard
- QBR template & first report
- Optional retainer scope
Typical tools
- Evidently · Arize
- Notion · Confluence docs
- Loom walkthroughs
- Slack office-hours channel
- Jira · Linear for ops tickets
Working session
60 minutes. Open decision log, live demos, blockers surfaced. Decisions dated and owned.
Stakeholder review
Metrics against success threshold. Risk register updates. Scope-change discussion if any.
Executive brief
One-page brief to sponsor: outcomes, spend, risks, decisions needed. Read in 4 minutes.
Business review (transformation only)
Board-ready. Outcomes, unit economics, next-quarter plan. Gates the next quarter's commitment.
Decision log
Dated, owned, reversible. No decision gets made verbally. Everyone can read what was agreed and why.
Risk register
Live document. Regulatory, technical, operational, vendor risks. Each with mitigation and owner.