How We Build
Our multi-agent delivery pipeline, in the open — because proof beats claims.
We don't just use AI tools — we run a multi-agent pipeline as the way we deliver, in the open. This is what earns trust with technical buyers.
The delivery pipeline
Specialized agents analyze, plan, do, and review — with a human on every quality gate.
flowchart LR
L[🗂️ Your codebase / problem] --> AN[Analyzer]
AN --> PL[Planner]
PL --> WK[Workers]
WK --> RV[Reviewer]
RV -->|fails| PL
RV -->|passes| HU[Human review]
HU --> OUT[✅ Shipped]
KB[(Knowledge base)] --- AN
KB --- WK
classDef a fill:#1414be,stroke:#7fb2ff,color:#fff;
classDef io fill:#001955,stroke:#00cfff,color:#fff;
classDef ok fill:#3a7d00,stroke:#b9e119,color:#fff;
class AN,PL,WK,RV a;
class L,KB io;
class HU,OUT ok;
Why agents, not a single prompt
A real agent plans, acts, remembers, and evaluates before it's done — the difference between a demo and a system.
flowchart TD
G[Goal] --> P[Plan]
P --> A[Act / tool use]
A --> M[Memory]
M --> E[Evaluate]
E -->|meets bar| D[Done]
E -->|not yet| P
classDef s fill:#1414be,stroke:#00cfff,color:#fff;
classDef d fill:#3a7d00,stroke:#b9e119,color:#fff;
class G,P,A,M,E s;
class D d;
Eval-first, human-in-the-loop
Evaluation harnesses
Measure whether the system is actually improving — not whether one demo worked.
Human review
Sits on the critical gates. Agents accelerate; people stay accountable.
Observability & guardrails
Cost, latency, and safety tracked on every run.
EGITECH
Have an AI idea to build, or a legacy system to modernize? Let's scope it with our multi-agent team.
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