Built for multi-agent workflows

Make multi-agent AI workflows actually work.

One agent alone remembers nothing between sessions, but a human fills the gap. Several agents handing off the same work to each other have no one to fill it. Venkai is the memory layer that survives the handoff.

Venkai's Semantic Modification Engine (SME) lets a chain of AI agents understand a system before any of them modify it — the decisions already made, the constraints already learned, the mistakes already found. Starting with software engineering — the one domain where a wrong recall is caught for free, not shipped to a customer. This is not built for occasional AI use; it is infrastructure for teams running multiple agents against real work, every day.

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The problem

AI agents are powerful alone. They fail when they need to collaborate.

One assistant, one conversation, one human filling in the gaps — that already works today. The failure shows up the moment a second agent, a second model, or a long automated workflow needs to pick up where the first one left off, and nothing tells it what already happened.

They forget your customers

Who they are, what was promised, what went wrong in March and how you fixed it. Every conversation re-explains the account from scratch, to a colleague who was there last time and no longer remembers.

They forget your processes

How work actually gets done here — not the version in the handbook, the version people follow. It gets re-described in every prompt, slightly differently each time, and every difference is a decision made on different facts.

They forget your decisions

That approach was tried in 2024. It failed, for a specific reason, and someone senior decided never again. Nobody wrote it down, so the AI proposes it again this week, confidently.

They forget how you work

Your constraints, your standards, the things that are non-negotiable because of a client, a regulator or a scar. The model has none of it, so it optimises for what it does know: everyone else's average.

What Venkai is

Venkai fixes the missing memory layer.

Venkai remembers what matters about your company and gives that understanding to the AI systems you already use.

It does not store your documents — your systems already do that. It builds the understanding underneath them: which decision produced which rule, what depends on what, what changed last quarter and what that broke.

Then it hands that understanding to whichever models you run. Same subscriptions, same assistants, same vendors. They simply stop arriving as strangers every morning.

The model is the employee. Venkai is everything that employee remembers.

Not a chatbot

There is no window your team logs into. Venkai has no interface for people — it has one for the AI systems your people already use.

Not a search engine

Search returns documents that contain a word. Venkai returns what your company decided, and what that decision still constrains today.

Not RAG with extra steps

Retrieval finds passages that look similar to a question. Looking similar is not understanding, and a paragraph is not a reason.

Not a replacement for your AI

Venkai does not sell you a model. It makes the ones you already chose worth more, and it keeps working when you change your mind about them.

The difference

One missing step changes the whole outcome.

Without Venkai

Your company
AI
Forgets

Every session starts at zero. Whoever is typing supplies the context, so the answer is only ever as good as what that person already knew.

With Venkai

Your company
Venkai memory
AI
Understands

Context comes from the system, not from whoever happens to be typing. What one team teaches it, every team inherits.

Where the others stop

Three ways to give an AI context. Only one of them lasts.

We use the first two every day. They are not strawmen — they are good tools that were never built to hold what a company knows.

Traditional AI

Smart but forgetful

Reasons brilliantly about anything public, and knows nothing that was not typed into this conversation. Close the window and it is gone.

Traditional RAG

Finds information, misses the meaning

Retrieves the paragraph that mentions your rule. It cannot tell you which decision created that rule, what it depends on, or that it was reversed in April.

Venkai

Remembers, understands, supplies context

Holds the relationships between what your company knows — decisions, constraints, dependencies, history — and serves them to your AI at the moment it acts.

How it works

Three moves. None of them ask your team to change how they work.

01
Connect
Point Venkai at what your company already produces — repositories, documents, tickets, schemas, records, threads. No migration, no new system to file things in. If your team has to change a habit for this to work, it does not work.
02
Understand
Venkai builds the relationships, not another index: what depends on what, which decision produced which rule, what changed and what it broke. This is the hard part, and it is the whole product.
03
Remember
Every AI system you run asks Venkai before it acts. Same models, same tools, same vendors — now answering with years of company context instead of an empty prompt window.

The assistant proposes a change a senior engineer killed eighteen months ago, for a reason nobody wrote down. Someone catches it in review — or nobody does.

With a memory layer

It knows the decision, the reason, and what it constrains. It proposes the change that respects them, or explains why it cannot.

Reduce repeated work.
Keep company knowledge alive.
Make AI more useful over time, not less.

Under the hood

For the reader who now wants the vocabulary.

Everything above was the plain-language version. This is the same system, named properly. Nothing here is aspirational — the diagram that follows marks exactly which stages run today and which are specified.

Venkai Kernel

The execution core. An intent is admitted or refused, becomes a process with a state, and cannot reach completion without passing verification. That is a property of the state machine, not a rule someone remembered to apply.

Semantic Memory Engine

Turns source material into structure: entities, their spans, their scope and what they belong to — resolved from the grammar, not guessed from the text.

Context Graph

The persistent relationships: definitions, references, dependencies. This is what turns a sentence naming one thing into the bounded set of places that may legally change because of it.

Intent Layer

A model describes what it wants; the layer derives the minimum-cost operation that satisfies it. The model never emits the change itself, so it cannot quietly rewrite what it did not mean to touch.

Agent Context Runtime

The interface any AI system queries before it acts, regardless of which vendor's model sits behind it. Vendor neutrality is not a feature here — it is the condition the whole thing exists under.

Enterprise Knowledge Fabric

Extending the same structure beyond code, so specifications, schemas and records participate in the same resolution. The interface is specified against the graph that exists; the extractors for non-code sources are the work ahead.

Built for the next generation of enterprise AI systems.

Architecture

Seven stages. Five of them run today.

The actual shape of the system, not an illustration of one. Operational runs today and is covered by the benchmark below. Specified is specified, not built yet.

01 · INPUTOperational
Sources
Source trees, documents and structured records, read where they already live. Nothing is uploaded and nothing is mirrored — the engine runs on the machine that owns the files.
Local filesystem · Git working tree
02 · PARSEOperational
Structural extraction
Each file is parsed into a concrete syntax tree, not scanned as text. Symbols, their spans, their decorators and their enclosing scope are resolved from the grammar, so a method keeps its class and a decorator stays part of the symbol it decorates.
AST parser · symbol resolver
03 · RELATEOperational
Symbol & dependency graph
Symbols are linked into a persistent graph of definitions, references and call edges. This is what turns an instruction that names one function into a bounded set of places that can legally change.
Symbol graph · project_graph.json
04 · REASONOperational
Semantic operation engine
A model describes an intent; the engine turns it into a typed operation — replace, insert-after, delete-range — and derives the minimum-cost plan that satisfies it. The model never emits the code itself.
Semantic operation schema · minimal-patch planner
05 · PROVEOperational
Verification & rollback
Before anything touches disk: refuse on an ambiguous anchor, refuse when a decorator would be dropped, re-parse after applying, and roll the transaction back if the file no longer compiles. Failure is a refusal, never a partial write.
Neuro-symbolic guard · transactional apply
06 · GENERALISESpecified
Knowledge representation
Extending the same graph beyond code, so that specifications, schemas and prose participate in the same resolution. The interface is specified against the existing graph; the extractors for non-code sources are not built.
Specified — not implemented
07 · SERVESpecified
Agent & application interface
A model-agnostic API so any agent can describe a change instead of writing one. Today the engine is driven in-process by our own tooling; the packaged interface, the installer and account linking are being built.
In development

Where we start

We started with code, because code is the only place memory can be proven.

Everyone claims their AI understands the business. Nobody can check. Ask a model to recall a marketing decision and a confident wrong answer is indistinguishable from a right one — you find out a quarter later, if ever.

Code does not have that problem. If the system misremembers what a function is or where it is used, the change does not compile, the test goes red, the guard refuses. Wrong recall is caught in under a second, by a machine, for free.

So we built the memory layer for the one domain that grades its own homework. It runs today: a structured map of a real codebase — what exists, what depends on what, what changed — that models consult before they act instead of guessing.

The measured result is on this page, with the command that reproduces it. It proves the layer works. It is not the whole of what we are building.

symbols held in the semantic graph: 88,663symbols held in the semantic graph
execution flows mapped across the codebase: 300execution flows mapped across the codebase
files indexed — a real system, not a demo corpus: 5,188files indexed — a real system, not a demo corpus

A model with the memory, and the same model without it

“The timeout in fetch_manifest is too short for large manifests — make it 30 seconds.”

Without memory the model has no name for the thing it is changing, so it re-emits everything around it and hopes nothing else moved. With memory, it points.

No memory of the codebasethe whole symbol, re-emitted

def fetch_manifest(url: str, retries: int = 3) -> dict: """Fetch the remote manifest and validate it.""" session = _session_for(url) last_err = None for attempt in range(retries): try: resp = session.get(url, timeout=30) resp.raise_for_status() except RequestException as err: last_err = err time.sleep(backoff(attempt)) continue data = resp.json() if not isinstance(data, dict): raise ManifestError(f'malformed manifest: {url}') return data raise ManifestError(f'{retries} attempts failed')

17 lines · 147 tokens

Querying Venkai firstthe change, named

try:- resp = session.get(url, timeout=10)+ resp = session.get(url, timeout=30) resp.raise_for_status()what the model actually emits:

edit("core/manifest.py",
     "timeout=10", "timeout=30")

→ resolved against the graph, refused if the anchor
  is ambiguous, rolled back if it no longer compiles.

1 character · 19 tokens

−87% on this specific example — not −95.9%. The function above is short so that it fits on screen, and short symbols are our worst case. We show it rather than the flattering one. Across 25 real symbols the figure is −95.9%, with 100% of edits applied or explicitly refused — never silently wrong.

Then what? Code first, then the structured record around it — schemas, contracts, tickets, decisions. Then the rest of what a company knows. Each step inherits the same rule: the system must be able to be wrong out loud, and refuse.

/semantic-modification-engine →

Evidence & Verification

Silent damage rate

Autonomous agents executing without governance introduce high rates of silent damage — actions that succeed syntactically but violate business rules or architectural policies.

Silent damage rate — autonomous AI agent execution

0.5% vs 53.6%

Silent damage rate (Venkai governed vs direct agent)

Direct un-governed agent execution(silent damage rate)	53.6%
Venkai governed execution channel(verified & recorded)	0.5%

$ python benchmarks/edit_damage_bench.py --limit 300 --offline

Measured 2026-08-04. Measures unverified modifications, unauthorized data accesses, and policy violations that execute without raising runtime exceptions. Figures are the silent rate among cases where damage was structurally reachable (455 cases run, 207 in reach) — a narrower, harder number than the raw silent rate across all cases. Full breakdown: /benchmarks/.

Secondary metric — token payload efficiency per edit

−95.9%

token reduction per execution

Model rewrites whole file / symbol(raw output tokens)	17,553
Venkai governed semantic call(governed delta)	713

$ python benchmarks/see_bench.py --limit 25

Measured 2026-08-03. Measured token output reduction when passing governed semantic diffs versus full-file re-generation.

Empirical proof stack. Direct agent execution without governance results in 53.6% silent damage. Venkai observation and enforcement channels reduce silent damage to 0.5% while optimizing payload efficiency by -95.9%.

/benchmarks — full methodology →

OBSERVATION MODE

Observation Mode

It watches before it blocks.

Nobody installs a system that starts blocking their teams on day one. Venkai starts in observation mode — measuring risks, recording proof, and generating clarity before enforcing any policy.

Phase 1 — Day 1 to 30

Zero disruption

For 30 days, the complete governance pipeline runs in parallel: intent evaluation, policy checks, routing, verification and audit recording. Nothing is blocked.

Phase 2 — Day 30 Report

The complete AI audit map

At the end of observation mode, you receive something your company never had before: a verified map of what your AI actually did, what would have been refused, and why.

Phase 3 — One-Click Control

Flip to enforcement

When the report stops surprising you, enforcement becomes a simple switch — the exact same rules and code path, now actively protecting your systems.

Observation Guarantee

"Venkai measures before it blocks. Nothing in your infrastructure breaks on day one."

Engineering

How the system is built, including what is not built.

There are no customer logos on this page and no testimonials, because we are at the beginning. What we have instead is a system you can watch run and a benchmark you can re-execute on your own code in about a second.

Fails safe, every time

Ambiguous edit, dropped decorator, file that won't re-parse — the engine refuses instead of guessing, and a refusal counts as a failure in our own benchmark. No partial writes, ever.

Measured, not asserted

Token cost, wall clock and success rate are logged per operation. Every figure on this page names the command that reproduces it.

Runs on your machine

No source material uploaded, no keys proxied through us. The benchmark runs with the network off — relevant whether you're a solo dev or a regulated enterprise.

API-first — in progress

Driven in-process by our own tooling today. The model-agnostic interface, installer and account linking are being built.

Not shippable today.

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.

Built for the enterprise

A memory layer sees everything. That is the entire security conversation.

We are early, and we are not going to pretend otherwise on this page. Here is what is designed in from the start, and what is still ahead of us.

Your knowledge stays yours

Venkai is not trained on what your company knows, and your knowledge does not leave your boundary to improve anyone else's model. The memory is yours, singular — never a pooled corpus.

Vendor-neutral by construction

Venkai does not sell you a model. It makes the ones you already chose better, and it keeps working when you change your mind about them — which you will.

Permissions are inherited, not reinvented

The layer must never become the hole in your access control. What a person cannot see, an AI acting for them cannot recall. This is a design constraint, not a roadmap item.

Refusal over confident guessing

The engine that runs today refuses an ambiguous operation and rolls back a change that breaks. That behaviour is the point, and it carries forward: a memory that guesses is worse than none.

No customer logos, no certifications we have not obtained, no numbers without the command that produces them. Everything on this site that can be checked, can be checked. Security posture · security@venkai.fr

Where this goes

By 2035, every company will run a memory layer. Most will not call it that.

The direction is not in question. Every employee will work alongside AI; that AI will act, not just answer. The only open question is whether all of it shares one understanding of the company, or each holds a private, slightly wrong copy.

A company with a memory layer compounds: every decision, every failure, every correction makes every future answer better. A company without one re-explains itself to a machine every morning, forever, and gets a marginally different answer each time.

That layer is infrastructure. It sits below the models — which will keep changing, and should — and above the data, which was never the hard part. It is the smallest permanent thing in a stack where everything else is replaceable, which is exactly why it is worth owning.

The objection: if this matters, will a foundation-model lab just ship it? They will ship storage — a bigger context window, a longer-lived thread. What they will not ship for you is domain-specific verification: the guard that knows which anchor is ambiguous in your codebase, which change violates your constraints, which recall is wrong before it ships. Raw memory is becoming a commodity feature. Verified, domain-grounded understanding is not — that is where this is built to sit.

Models are becoming a commodity. Memory is becoming the moat.

Leadership

Who is building this

Engineering governance infrastructure demands radical transparency, reproducible benchmarks, and verifiable architecture.

Enzo Laurence

Founder & Lead Engineer

Venkai is built by Enzo Laurenceau–Lhérisson.

The project is built around measurable engineering:

Every measurable claim is tied to a reproducible command.
Every architectural decision is documented before implementation.
Every benchmark publishes limitations alongside results.

Questions

Questions about giving AI a memory

Written to be quoted — by a person forwarding this page, or by an assistant answering for us.

What is Venkai?

Venkai is AI Memory Infrastructure — the layer that holds a company's decisions, constraints and relationships so any AI system can use them, instead of starting over each conversation.

Why do AI models forget company knowledge?

A model is trained once on public data, then given only what fits in one conversation. Nothing about your company is stored between sessions unless something outside the model keeps it.

Is Venkai just RAG?

No. RAG retrieves documents that resemble a question. Venkai models relationships and history — which decision caused which constraint, and whether it still holds.

Can Venkai work with the AI models we already use?

Yes. Venkai is model-vendor neutral by design — it supplies context to whatever AI systems a company already runs, and keeps working if those choices change.

What happens when the memory itself becomes wrong?

Venkai deploys in observation mode first: it records what it would have answered or blocked, without acting, so errors surface as a report before they reach a real decision.

All questions →

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