Engineered for production.
Every engagement follows a disciplined, transparent methodology. From discovery through deployment, you see exactly what's happening, what's been tested, and what happens if something goes wrong.
Five phases. No mystery.
Every engagement follows the same structured process — defined milestones, transparent progress, and no scope creep.
Discover
Map your workflows, infrastructure, and data landscape. Identify where AI agents create the most leverage for your business.
Design
Architecture decisions, cloud platform selection, and technical roadmaps scoped to your constraints and compliance requirements.
Build
Iterative development alongside your engineers, building on Google ADK, Azure AI Foundry, or AWS Strands. Phased delivery so you see working automation early.
Test
Four-phase testing strategy — internal testing, client UAT, pilot deployment, and formal sign-off.
Deploy
Go-live with monitoring, alerting, handover documentation, and role-based training. Your team is equipped to operate day-to-day.
Built for non-deterministic systems.
AI agents aren't traditional software. Our quality framework is designed for systems where the same input can produce different outputs.
We run a four-phase testing process before any agent reaches your production environment. Each phase catches issues the previous phase cannot.
Internal Testing
Functional testing across happy path, edge cases, and error handling. Security and adversarial testing including prompt injection and data leakage. Output quality evaluated against golden baselines.
Client UAT
Staging environment deployed identically to production in your cloud. 15–30 scenarios mapped to your real workflows, tested with real data and edge cases only you know about.
Pilot (2–4 weeks)
Agent runs in production alongside your existing process — not replacing it. Both outputs compared daily. Real-world volume surfaces edge cases testing missed.
Sign-off
All UAT scenarios passed. Pilot ran 2+ weeks above 95% success rate and 4/5 quality score. No unresolved P1 or P2 bugs. Docs delivered, AI champion trained.
Every layer independently reversible. No single failure requires more than minutes to recover from.
| Layer | Method | Recovery |
|---|---|---|
| Prompts / config | Revert to previous version in registry | Seconds |
| Application containers | Redeploy previous image tag | Minutes |
| Database schema | Migration downgrade (every migration has a working downgrade) | Minutes |
| Database data | Cloud-native point-in-time recovery (30-day retention) | Minutes to hours |
| Infrastructure | Terraform revert and apply from git history | Minutes |
| Vector indexes | Snapshot before re-indexing, revert to previous snapshot | Minutes |
Error Monitoring
Sentry integrated into every deployed agent. Application crashes, unhandled exceptions, and runtime failures trigger immediate notifications to our engineering team.
Output Quality
LLM-as-judge scoring against golden examples, human feedback tracked per agent, and regression detection against baseline outputs before any update reaches users.
Infrastructure Drift
Managed through Terraform. Scheduled plan runs detect any manual changes made outside our IaC pipeline, triggering an immediate alert and reconciliation.
Model Drift
Foundation model versions are pinned (e.g. gemini-2.5-flash-001, never latest). Evaluation suites run on schedule, and model upgrades are deliberate and tested — never automatic.
See our methodology in action.
Walk through a real engagement from discovery to production with our team.