How we build it
Envoy, and the ideas
it's built on
This is the system we test our own ideas in: Envoy, the assistant layer, and Envoy Warehouse, the governed data layer underneath it. Here's how each piece works and why we made it that way.
The idea
An ontology is just a clear model of how a world fits together.
A general model is fluent but rootless — it doesn't know a given organisation's terms, its systems, or what its numbers actually mean, so it improvises. The bet we're testing is that giving it roots — an explicit model of the domain to answer from — beats prompting harder. Everything below is what that looks like when you build it properly.
The platform
Two halves, one foundation
The surface people talk to, and the governed data layer it stands on — joined by a shared model of the domain.
The surface · Envoy
The layer people actually talk to
A conversational workspace with documents, agents, and skills — the place where the ontology has to earn its keep in ordinary use.
The foundation · Envoy Warehouse
The governed data layer underneath
Data modelled as typed objects and named metrics computed deterministically — served to any AI through a locked-down MCP server. Trustworthy numbers, never raw rows.
AI that speaks the local language
Every organisation has its own vocabulary, and generic assistants flatten it. So Envoy carries the domain's terms into retrieval, into the agents, and into how answers get cited. The design rule we hold ourselves to: it should make people better at their own work, not stand in for them.
How the skills workquery_metric(activity, average · segment) · ~134 tokens · cited
Conversational workspace
A clean chat interface your team already knows how to use — no training required.
Your documents, retrieved
Upload to a workspace; Envoy retrieves and cites the right passages with hybrid search (RAG).
Agents, tools & skills
Ready-made agents out of the box, plus bespoke ones tuned to your terminology and workflows.
Workspaces
Spaces tailored to each team — each with its own documents, agents, and context.
Deep integrations
Connects to the systems you already run — CRM, accounting, storage, comms, data — via a real integration framework.
Admin console
Your team manages users, branding, agents, tools, and usage themselves. No black box.
Specialist agents
Agents built for the actual job
Not one generic chatbot, but focused agents — each tuned to a real task and powered by composable, portable skills. These are the ones running today; the list below is a fair picture of what the pattern is good at, and where it needs a specialist to sit beside it.
Contract Review
Compares an AS 4902 contract against your organisation's preferred positions clause-by-clause, flags departures from your playbook and the standard's defaults, and renders a departures table right in the chat.
Tender Response
Draws on your past tender responses to draft consistent, well-supported answers — a single focused question, or a whole tender at once — ready to review and paste.
Commercial Contract Review
LegalReviews vendor agreements, MSAs, and SaaS subscriptions against your Legal Playbook — a deviation memo with severity, redlines, and approval routing.
Privacy / DPA Review
LegalReviews Data Processing Agreements term-by-term; detects processor vs controller direction and flags transfer mechanisms and sectoral overlays.
Employment Review
LegalReviews offer letters, employment and contractor agreements against your Legal Playbook, with jurisdiction-specific research.
NDA Triage
LegalFast GREEN / YELLOW / RED triage of inbound NDAs — so only the ones that genuinely need lawyer time get routed there.
IP Clause Review
LegalReviews IP clauses — assignment, licence, warranties, indemnities — against your playbook, with assignment-gap detection.
Accounting Review
FinanceReviews contracts for accounting implications — revenue recognition, leases, modifications — under IFRS, US GAAP, or AASB.
Research Analyst
GeneralCombines web research with your workspace documents to produce sourced, structured briefings.
Summariser
GeneralShort, medium, or detailed summaries of any document you upload.
Curious how one of these is put together? Ask and we'll walk you through it →
Skills
Composable know-how, yours to keep
A skill is a small, open SKILL.md file that teaches an agent one job — when to step in, how to do it, which tools to use, plus any reference files it needs. Agents load them only when relevant. Some run on demand; others are always on, encoding conventions like citation discipline and locale.
legal-playbook-setup
On demand
An interactive interview that captures your team's positions on NDA terms, vendor contracts, escalation, and delegations.
nda-review
On demand
Fast GREEN / YELLOW / RED triage of inbound NDAs, so legal time only goes where it's genuinely needed.
extract-tender-questions
On demand
Parses a tender RFT into a structured list of the questions and criteria a bidder must answer.
standards-reference
On demand
Cites and applies external standards correctly — IFRS, US GAAP, AASB, and data-protection regimes.
research-brief
On demand
Produces a structured research brief: background, key findings, sources, and open questions.
document-extraction-helper
On demand
Pulls structured data — tables, key/value pairs, entities — out of PDFs, CSVs, and spreadsheets.
source-fidelity
Always on
Strict citation discipline: quote the source verbatim, then paraphrase. Never invent a figure.
aussie-english
Always on
Locks the conversation to Australian English spelling and conventions.
user-locale
Always on
Carries the user's locale so numbers, dates, and currency are formatted consistently.
Export — take it anywhere
Download any skill as a name.zip — its SKILL.md plus every reference file and asset. Because it's the same open Agent Skills format Claude uses, it runs in Claude (Code & API) and other agent tools, not just Envoy.
Import — bring your own
Add a skill by pasting its SKILL.md, uploading a .md or .zip, or filling in a guided form. Envoy lints it, versions it, and tracks every change — so your team can author, fork, and refine skills with no lock-in.
Numbers AI can actually trust
Envoy Warehouse ingests your data and models it as an ontology — typed objects, named metrics, and dimensions. Metrics are computed deterministically in the database and served to AI through a locked-down MCP server, so models reason over governed facts instead of guessing from raw tables.
- Deterministic metrics. Every number is computed in Postgres from a defined formula — same question, same answer, every time.
- Token-efficient by design. A full management summary is ~134 tokens, not thousands of rows — fast, cheap, and within context.
- Locked-down MCP server. Nothing is discoverable without a credential. Connect Claude (Desktop, web, Enterprise) via token, OAuth, or Microsoft Entra SSO.
- Configuration, not rewrites. Onboarding a new client or domain is a new ontology config — the generic core never names a client.
A design principle
What should be specific, and what shouldn't
Vocabulary, workspaces and skills belong to the domain and should be configurable. The engine underneath shouldn't know a single customer's name — the moment it does, every fix becomes a fork.
Vocabulary is configuration
Terms, documents and working conventions live in the ontology config and the workspace, not in the code — so they can change without a release.
Narrow beats general
A tightly scoped agent that knows one job and one vocabulary reliably outperforms a general assistant given the same task. This is the clearest pattern we've found so far.
Skills that travel with you
Skills are open SKILL.md files — the same format Claude uses. Export them and they run in Claude (Code & API) and other tools, not just Envoy. Know-how someone wrote should outlive the tool it was written in.
See how export & import work →Trust & security
Your brand, your data, honest answers
Security is structural — enforced by the database and the protocol, not by hopeful application code. And because the numbers come from the warehouse, the AI can't make them up.
Your brand and domain
Runs white-label under your logo, colours, and domain. Your team and customers see your product, not ours.
Schema-per-tenant isolation
Each tenant's data lives in its own PostgreSQL schema — isolation enforced by the database itself.
Encrypted credentials
Integration credentials are encrypted per tenant; authorisation is explicit on every action.
Deterministic, cited answers
Numbers are computed in the warehouse and cited. Same question, same answer — no hallucinated metrics.
Locked-down MCP
Nothing is discoverable without a credential — token, OAuth, or Microsoft Entra SSO. Every call is scoped.
Full observability
Every agent run, tool call, and cost is captured — so you always know what your AI is doing.
The method
The order we've learned to do it in
Modelling before building, and handing over the controls before anyone gets attached to us holding them. Take it and use it — the sequence matters more than the software.
Model it
Learn the problem and the words people already use for it, then write that down as an ontology. Skipping this step is where most AI projects quietly go wrong.
Wire it
Set up the agents, metrics, tools and integrations against that model — configuration, not a new codebase each time.
Let go
Hand the controls to the people doing the work. If they can't change it without us, we built it wrong.
The Ontology AI
Take the ideas, and tell us where we're wrong
None of this is settled. If you're building something in the same territory, or you think a decision here is a mistake, we'd genuinely like to hear it. We keep publishing what we learn either way.