Technical pilot
What a pilot with Venkai actually covers
One page, stated plainly, before anyone's time is spent: what runs today, what a pilot asks of you, and what is still ahead of us.
Technical pilot
Who this is for
- Engineering teams shipping into a codebase large enough that context gets re-explained every week.
- AI-first companies already running agents against their own code and feeling the reliability ceiling.
- Software agencies where the same context-gathering repeats at the start of every client engagement.
Technical pilot
Pilot format
A 30-day technical evaluation on one workflow, in observation mode: Venkai records what it would have supplied or refused, without acting, so the decision to expand is based on a report your team reviewed.
What gets measured during the 30 days
- Agent reliability — how often a governed change matches intent versus an ungoverned baseline.
- Context preservation — whether the same fact, once given, has to be re-explained.
- Development efficiency — token and time cost of a governed change versus your team's current baseline.
Technical pilot
Scope, stated honestly
Today, live: the SME (Semantic Modification Engine) — AST-anchored code understanding and modification, benchmarked on the corpus published at /audit/. This is what a pilot runs on.
Not yet built: the enterprise memory layer extending this beyond code to specifications, schemas and prose. A pilot does not depend on it — it exists to prove the pattern generalizes, one workflow at a time.
How you start
Start with one workflow. Scale into enterprise intelligence.
Nobody connects an entire company to something new. So you do not. You pick one workflow that already costs you repetition, and you measure what changes.
Identify a valuable workflow
One process where the same context gets re-explained every week, and where being wrong has a cost you can name.
Connect your company knowledge
Point Venkai at the sources that workflow already depends on. Nothing is migrated, and nothing is uploaded anywhere you did not choose.
Deploy the context layer
Your existing AI systems start consulting it — in observation mode first, so it measures before it changes anything.
Measure the impact
Repetition avoided, context supplied, mistakes caught before they landed. Written down with the method, including the parts that did not improve.
Expand
One workflow becomes a department; a department becomes the company. Each addition makes every earlier one work better — which is the entire reason this is infrastructure and not a tool.
Run the proof yourself
Every figure on this site names the command that reproduces it. /audit/ is the same engine and benchmark corpus, reassembled into the interface we run in front of a client — reproducible before you commit any time.
/audit →Early access
Give your AI a memory of your business.
We are onboarding a small number of teams that already run AI at enough volume to feel what is missing. Early access means direct influence on what gets built, and pricing that reflects showing up first.
No drip sequence. No newsletter. We write when there is something to show. · or email directly at contact@venkai.fr