Built to fit the workflow as it actually runs.
We work with your operations leads and frontline staff so the agent matches the real process, exceptions and all.
Built alongside your team.
Our methodology embeds engineering capability alongside the people who already know your workflow — your operations leads, department heads, and the staff who run the work day-to-day. We bring the AI and software expertise.
Our Methodology
Three convictions that shape every engagement.
The five-phase delivery sits underneath three principles we won't bend on. They shape who we work with, when we deploy, and what happens after we hand over.
Earn trust before autonomy.
Every agent runs in shadow mode against the existing process before it gets trigger access. We don't deploy what we haven't proven equivalent first.
Build for handover, not retention.
Day-to-day operation belongs to your team — that's the design constraint, not an afterthought. Documentation, role-based training, and an internal AI champion mean your operation isn't dependent on a Slack channel with us.
Delivery
A disciplined path from first call to production.
Five phases. Defined milestones, transparent progress, no scope creep. Each phase is designed so your team is in the room — even when they don't write code.
01
Step 01
Discover
We work with your operations leads, department heads, and frontline staff to map the current workflow and its exceptions.
02
Step 02
Design
We translate what we observed into an architecture scoped to your cloud, compliance, and budget constraints. Your team sees exactly what the agent will and won't do — in plain language — before we build a line of code.
03
Step 03
Build
Our engineers do the heavy lifting on Google ADK, Azure AI Foundry, or AWS Strands. Your team reviews working slices each week so the agent matches how the work actually gets done — not how a process diagram says it should.
04
Step 04
Test
Your team runs UAT against the real scenarios they've seen go sideways — no technical setup required. A pilot runs in parallel with the existing process so the agent earns trust before it earns trigger access.
05
Step 05
Deploy
Go-live with monitoring, alerting, role-based training for non-technical operators, and an internal AI champion. Your team is equipped to operate day-to-day — without needing engineers to keep the lights on.
Quality & Reliability
Built for non-deterministic systems.
AI agents aren't traditional software. The same input can produce different outputs — so our quality framework is built around that fact, not against it.
Testing · 01
Four phases before production.
Each testing phase catches issues the previous phase cannot. No agent reaches your production environment until all four pass.
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 your team knows about.
Pilot
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 sustained above 95% success rate and 4/5 quality score. No unresolved P1 or P2 bugs. Docs delivered, AI champion trained.
Rollback · 02
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 |
Monitoring · 03
Errors and quality, watched continuously.
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.
Drift · 04
Infrastructure and models, pinned and audited.
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.
The next step
Pick a time. Let's talk.
A 15-minute introductory call — no pitch deck, no obligation. We'll tell you straight whether AI agents are the right fit for what you're trying to do.