We embed a senior AI-native pod inside your organization and own delivery from roadmap to production.
Among the first in the world certified to ISO/IEC 42001 for AI Management Systems. Four years of AI-native engineering, our own maturity framework, outcome-based pricing. Phase 1 memo in 14 days. Walk away after any phase — keep every artifact.
Engineers inside your VPC, repos, ticketing and standups. Not a delivery center behind a PM.
We design the workflow, data layer and human-in-the-loop assuming AI is already part of the system.
You engage a pod of 3–6 senior engineers assembled around your stack. Roles are fluid, not per CV.
The pod owns production deployment, adoption metrics and rollback. Not tickets closed.
- Phase 1 · Discovery2 weeks · fixed price · a written technical memo your engineers could implement from.
- Phase 2 · Build6–12 weeks · milestone-priced · code in your repo from day one.
- Phase 3 · OperateMonthly retainer · same pod, different SLA · 30 days notice either way.
- Walk-away clauseStop after any phase. Zero penalty. You keep every artifact.
Three shapes most clients pick.
Same delivery model. Different surface area. We will tell you which one fits in the Phase 1 memo.
AI Transformation Partner
- AI-Native maturity assessment + roadmap
- SDLC rebuilt for AI-native delivery
- Embedded AI engineering squads
- Governance framework + ISO 42001 support
AI-Enhanced Dedicated Delivery
- AI-Native FDE pod, 4–12+ engineers
- Fully AI-native delivery processes
- Predictive software engineering
- Fixed-price or outcome-based
AI Feature Acceleration
- GenAI features and agents into existing products
- Prototype → production in 4–8 weeks
- Integration with legacy systems
Every engagement still starts with the same 2-week Phase 1 memo. You walk away after any phase, with every artifact.
Numbers, reports, credentials.
- 20–0%
- Productivity gains measured against pre-pod baselines on the same teams.
- 0 pages
- The State of AI-Native Software Engineering: 2026 Industry Report — benchmarked against EPAM, Accenture and the global SI peer set.
- 0 patterns
- Anonymised production cases across fintech, logistics, healthtech and industrial — real numbers, sanitised names.
- ISO/IEC 42001 (AI Management Systems) — lead implementer
- ISO/IEC 27001 — lead auditor / implementer
- AWS Generative AI Competency
- AWS Solutions Architect Professional, ML Specialty
- Google Cloud Professional ML Engineer
- Microsoft Azure AI Engineer Associate
- CISSP, CSSLP — for security-bearing roles
These are certifications held by engineers in our pods, not corporate badges on a slide.
CTOs and VPs of Engineering
Three or more open senior reqs, an AI mandate from the board, and a roadmap that is already slipping.
Heads of Engineering
Pilots stuck between notebook and production because nobody owns auth, evals, observability, or rollback.
CIOs and Heads of Operations
A portfolio of internal workflows that look automatable but never get past PoC because the last mile has no owner.
If your bottleneck sounds like "we know roughly what we want, we just cannot staff a team that actually ships it" — this page is for you.
- Teams looking for hourly contractors to backfill a Jira queue.
- Companies looking to outsource an entire product line and walk away.
- Buyers comparing day rates across staff-aug vendors. We are not the cheapest option, and we will tell you when a cheaper option is the right one.
- Day 030-min scoping call.
- Day 1–3MSA, DPA, access.
- Day 4–14Phase 1 memo.
- Day 15–30Pod embedded. First commits.
Three phases. Fixed timeline. Walk-away clause.
Discovery in 2 weeks. Build in 6 to 12 weeks. Operate on a monthly retainer.
Eight patterns we deliver to production.
Company brains, detection platforms, entity resolution, agent runtimes, legacy slices, document decisioning.
The engineers who built it keep it healthy.
Same pod, different SLA. No ticketing queue staffed by people who never saw the code.