Agentic coding & automation, tailored

One-size-fits-all doesn't work. Your solution will.

We study your case, measure how ready your systems are, build your tailored agent pipeline, and stay until it runs the way it should.

Book a demo The method, in detail
The path
Initial consultation we learn what your team does, and what cannot go wrong
Readiness assessment systems, repositories, processes: what's ready, what isn't
Design & tuning your tailored graph: logic, flow, agents, gates, permissions
Corrective follow-up we stay alongside you through the first real runs
The problem

Why AI «alone» isn't enough

Unverified output

An unsupervised agent produces results nobody approved. That risk ends up in production.

Tools for the few

Agentic pipelines exist, but they demand tooling only their builders know how to operate.

Generic solutions

A complex system only works when tuned to your code, your processes, your constraints.

What we build

Agentic coding Agents that write code, a team in command A pipeline that carries a request all the way to a pull request, through architecture, code, verification and tests, with your team at the control points. Automation If it communicates, it can be orchestrated Graphs that orchestrate processes across systems and people. AI nodes only where they add value.

Inside the pipeline

Every pipeline is a different graph: yours. But it is always built from the same kinds of elements, combined and tuned to the case. Touch a type to find it in the mosaic:

MIDDLEWARE · ALWAYS ON
Agentic nodes invoke an LLM where reasoning is needed
Programmatic nodes deterministic logic, where it's the better tool
Human gates the graph stops and waits for your team
Approved outcomes nothing ships without a human yes
Middleware, around everything deterministic checks, cost and tool budgets, anomaly detection

Intelligence where it's needed, determinism where it counts, humans where decisions are made, and a control layer watching over everything, always.

A real run

What you see while the agents work

When a run ends, the pipeline opens a pull request and stops. Before you decide, you read the shared state: intent verified, tests, risks, every changed line. Then the gate is yours.

Shared state Run · export the runs list to CSV
CHECKLIST stream CSV feature test docs
INTENT CHECK match The diff implements the requested export endpoint.
FUNCTIONAL TESTS 12 passed · 0 failed
END-TO-END TESTS 9 passed · 0 failed
RISK REVIEW low No destructive migrations; read-only export.
1 file changed +5 −1
app/Application/Runs/RunsService.php
@@ -40,6 +40,12 @@ final class RunsService
public function list(User $user, array $filters): LengthAwarePaginator
{
- $query = Run::query();
+ $query = Run::query()->with(['pipeline', 'creator']);
+
+ if (! empty($filters['export'])) {
+ return $this->export($query);
+ }
return $query->paginate();
}
RUN COST $3.33 · 256.5 kTok Tracked on every run, budget enforced by the runtime. 
{
  "run": "…",
  "intent": "match",
  "tests": { "feature": "12/12", "e2e": "9/9" },
  "risk": "low"
}
Human review

Everything is ready for your final review.

NOTE
Add directions for the agents (required for some choices).
Approve and closeTear down the sandbox and finish the run. FeedbackRequest a corrective iteration driven by your notes. Resume and testWake the sandbox up to test the app yourself, then come back to decide.
Approve and close Feedback Resume and test

Dozens of teams chose us for one simple reason: always seeing what the agents are doing, and being able to say no.

Tailored pipeline

The agent graph is designed around your code and your processes. There is no «default» version.

Human gates

The pipeline stops wherever a decision is needed: approve, correct, or test it yourself.

Readable state

Plan, changed files, checklist, tests, intent check and risk review: all readable, in plain language, run by run.

It starts with a conversation.

Tell us about the process that costs you the most. We'll tell you, honestly, if and how a supervised pipeline can help.

Book a demo