From plain-English input to verified output, no manual steps in between.
Multi-stage AI pipelines where each stage has a defined role, defined outputs, and a quality gate before the next stage runs.
You have a repeatable specialist process with no clear path to automation.
Most workflows that look "too complex for AI" are sequences of well-defined sub-tasks, each with specialist knowledge embedded in human memory rather than system logic. The bottleneck is not the AI capability. It is the absence of proper scope, output definition, and quality verification.
We take any repeatable process with a definable output and build a multi-stage pipeline where each stage has a role, produces verifiable output, and passes a quality gate before the next stage runs. No drift. No hallucination in the output. No manual checking required.
Specialist runs the process manually. Result quality varies by person and day. No reproducibility. Cannot scale without headcount. Knowledge leaves with people.
Pipeline runs in seconds. Every output passes an eval harness. CI gate blocks any PR that drops quality. You review outcomes, not the work.
How we build it.
Every stage has a defined role and a quality gate before the next one runs, the same engineering spine under every Agentix system.
- 01
Discovery & Scope
2-hour session. We map every sub-task, define the output schema, identify failure modes, and agree the eval criteria. This determines the architecture.
- 02
Knowledge Base
All domain knowledge is structured into a Qdrant vector store with semantic search. Agents retrieve, they do not guess.
- 03
Pipeline Architecture
Each stage gets a specialist agent with a defined role. FastMCP creates the system boundary, no agent touches a live system without going through the gateway.
- 04
Eval Harness
100+ scenario stories covering happy path, edge cases, and all known failure modes. Pipeline must pass before any production deployment.
- 05
CI Gate
GitLab / GitHub CI gate on every PR. Score drops more than 5pp → pipeline blocked. No silent quality regressions in production.
- 06
Deploy & Monitor
Production deployment with Langfuse traces on every run, Prometheus metrics, and alerting. You see every execution.
Delivered on
every engagement.
You own all of it, code, infrastructure and data, from day one. No licence fees, no hosted dependency.
- MCP gateway with tool schema for every external system the pipeline touches
- Eval harness, 100+ scenario stories before any production deployment
- CI regression gate, PR blocked if eval score drops more than 5pp
- Knowledge base (Qdrant) for all domain knowledge the pipeline needs
- Human approval gates at defined decision points
- Full observability, Langfuse traces on every run, Prometheus metrics, alerting
Not a demo. In production.
Open-source.
Self-hosted. Owned.
Built on components you can host, fork and extend. LiteLLM abstraction means you swap models without rewriting integrations.
Is this your
problem shape?
Describe what you're trying to do. We'll confirm whether this is the right system type, scope the build, and tell you what the eval harness looks like.