Eleven stages.Eleven deliverables.
Engineering for production, not just prototypes. Every engagement follows a structured lifecycle, and each stage ends in a written deliverable you can inspect, question and approve.
The engineering lifecycle.
Data, AI and software engineering sit inside one sequence, so nothing falls between teams.
- 1
Client Business Problem
What you receive
Problem statement, success criteria and measurable outcomes agreed in writing
- 2
Discovery & Solution Architecture
What you receive
Solution architecture, technology options, risk register and delivery plan
- 3
Data Engineering
What you receive
Data audit, pipelines, quality checks and an AI-ready data foundation
- 4
AI Engineering
What you receive
Models, agents and RAG pipelines built against the agreed metrics
- 5
Software Engineering
What you receive
Production-grade application, APIs and integrations; code-reviewed and documented
- 6
Testing & Quality Assurance
What you receive
Automated test suites and QA reports
- 7
AI Evaluation & Validation
What you receive
Evaluation report: accuracy, robustness and failure modes against thresholds agreed up front
- 8
Security & Governance
What you receive
Security review, access controls, data-handling and compliance documentation
- 9
Cloud & Production Deployment
What you receive
Infrastructure as code, CI / CD pipeline and release runbook
- 10
Observability & Monitoring
What you receive
Dashboards, alerting, model-drift and cost monitoring
- 11
Continuous Improvement
What you receive
Regular performance reviews, retraining and roadmap updates
Four of the eleven deliverables.
From the first signed problem statement to the dashboard that watches the system in production. Illustrative samples.
Stage 1 · Problem statement
SignedBusiness problem definition
- Problem
- Claims staff spend 40% of their day sorting documents before any decision is made.
- Success measure
- Median triage time below 4 hours; no drop in decision accuracy.
- Out of scope
- Automated approval of claims. A person decides every claim.
ADR-007
AcceptedArchitecture decision record
Vector store for retrieval
- Context
- 2.4M documents, strict tenant isolation, data must stay in-region.
- Decision
- PostgreSQL with pgvector in the client’s own cloud account.
- Consequence
- One database to operate; re-index window planned monthly.
AI evaluation report
ApprovedSupport assistant (RAG) · release candidate 3
| Metric | Agreed | Result |
|---|---|---|
| Groundedness | ≥ 0.90 | 0.94passed |
| Answer accuracy | ≥ 0.85 | 0.88passed |
| Out-of-scope refusal | ≥ 0.95 | 0.97passed |
| Hallucination rate | ≤ 2.0% | 1.2%passed |
| p95 latency | ≤ 2.5 s | 1.9 spassed |
Production monitoring
LiveModel drift · last 30 days
- Uptime
- 99.97%
- Cost / 1k req.
- $0.42
- Alerts
- 0 open
Commitments that reduce your risk.
Start medium
A paid discovery sprint or fixed-scope pilot lets you judge our engineers on real work, before a larger commitment.
Agree success first
Acceptance criteria and AI-evaluation thresholds are set in writing before the build begins.
You own the work
Code and IP are assigned to you and held in your own repositories from day one.
Protected from the start
NDA and IP-assignment terms are signed before any technical exchange.
Visible progress
A named Senior Lead per engagement, written progress reports and milestone-based payments.
Enterprise-grade execution.
The delivery practices every international engagement is built on.
- Clear project scope
- Defined deliverables
- Transparent communication
- Milestone-based delivery
- Technical documentation
- Code-quality practices
- Testing and validation
- Secure handling of client information
- Intellectual property protection
- Professional contractual arrangements
- Confidentiality
- Long-term client relationships
Start medium: a paid discovery sprint or a fixed-scope pilot lets you judge our engineers on real work.