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Achilles

Vision

Where Achilles is headed: from a knowledge base toward corporate memory, from passive answers toward proactive help.

Today

Enterprise AI Platform

Today, Achilles is a company's single AI platform: a hub of providers, models, agents, and spend. The platform connects to the company's tools and builds a single knowledge graph — people, projects, tasks, and decisions linked in one place, with answers grounded on them.

Knowledge graph Agents Achilles Jira Slack Confluence
Sources connected — the platform builds the graph and manages the agents

A knowledge graph finds documents and links. But what if the platform didn't just find things — but understood why decisions were made?

Evolution

Three levels of corporate knowledge

Each level adds depth. Search finds documents. The graph links them. Context explains why everything is the way it is.

OTHERS ACHILLES NOW WHERE WE'RE GOING Search Knowledge graph Context what exists what's linked why it's so
Achilles is already at the knowledge-graph level. The next step is context
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Enterprise search
Indexes documents. Finds by keyword and semantics. Doesn't know the links between entities.
"ADR-015 exists"
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Knowledge graph — Achilles today
Links people, projects, tasks, documents, and channels. Knows who created what and how it's connected.
"@alex created ADR-015, linked to INFRA-891, discussed in #backend"
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Context — the next step
Understands the reasons, the history of decisions, the arguments on each side, the alternatives considered, and how projects evolved.
"PostgreSQL was chosen for ACID + write load. @alex proposed it, @john objected. 3 days of discussion, 2 alternatives"
Knowledge graph
Knows what's linked
@alex created ADR-015
ADR-015 is linked to INFRA-891
The discussion was in #backend
Doesn't know why the decision was made
Doesn't know what the alternatives were
Context
Knows why
Everything the graph knows
Why PostgreSQL: ACID + write load
@alex proposed it, @john objected
2 alternatives weighed over 3 days
INFRA-891 is a consequence of the decision
The graph knows what's linked.
Context knows why.
Corporate memory is a knowledge graph + an understanding of the reasons.

When the platform understands not just what happened, but why and what it led to — it unlocks possibilities beyond an ordinary graph.

Next level

Agents that act ahead of the curve

Today, agents answer queries. Tomorrow, a personal assistant agent with the company's full context will flag risks on its own, point you to the relevant experience, and save you time — before you ever hit the problem.

Context PROACTIVE AI agent Employee decisions, history, links, reasons proactive suggestions
The agent takes in the company's context and proactively helps the employee
→
You pick up task X — team Y tried a similar approach six months ago. Here's what they learned and why they changed direction.
→
Your sprint depends on team Z, which is in a feature freeze until Friday. Worth replanning PROJ-142.
→
You're writing an RFC — here are three decisions in neighboring teams that could conflict with your proposal.
→
A new customer ticket — a similar problem was solved in Q3; here's the context and the fix.
Today
Reactive agent
Only responds to a query
Knows what it found for the query
Doesn't see the context of current work
Tomorrow
Proactive assistant
Notices relevant context on its own
Knows the history of decisions and the reasons
Understands what the employee is working on
Suggests before being asked
Perspective

Knowledge stays — people change

When an employee leaves, the context leaves with them: why decisions were made, how processes work, what's already been tried and didn't work. Corporate memory solves this — knowledge isn't tied to people.

Working Leaves AI stand-in New hire context accrues context preserved covers the role inherits context context is continuous — knowledge isn't lost when people change
The lifecycle of a role: context stays with the company at every stage
Without context
Knowledge loss
A key employee leaves
The decision context leaves with them
The team repeats old mistakes
A new hire takes months to get up to speed
With context
Seamless rotation
The role's context is preserved in the system
An AI stand-in covers the routine during the transition
The history of decisions is available — mistakes aren't repeated
A new hire gets the role's context in hours
Zero bus factor
Knowledge doesn't depend on one person — the context is available to the whole company.
An AI stand-in during the search
While a replacement is being found, AI covers the routine functions of the role based on context.
Onboarding in hours
A new hire gets the full context of the role: decisions, reasons, processes, connections.
Institutional memory
The company remembers not only what was done, but why — even years later.