AI infrastructure for consulting

Consulting firms run on knowledge that mostly lives in people, and people leave. The analysis from a past engagement, the context behind a client's situation, the reasoning that shaped a deliverable, all of it tends to walk out the door when a consultant moves on, and the next team rebuilds what the firm already knew. If you're building knowledge management for consulting, the hard part isn't storing files, it's turning that scattered, departing institutional knowledge into something the firm keeps and can query, per client, without it leaking between them.

This page is for teams building tools for consulting workflows. Exabase gives you per-client isolation through Bases, extraction from deliverables and reports through Extract, institutional memory that persists regardless of who's on the engagement, and Workers that keep client knowledge bases current between engagements. Each client's knowledge stays sealed in its own environment, and the firm's knowledge stops depending on individual memories.

What you can build

Consulting knowledge tools tend to be one of a few shapes, each on infrastructure that already exists.

A per-client knowledge base that holds everything the firm knows about a client, deliverables, reports, context, decisions, isolated to that client and searchable, the self-maintaining knowledge bases pattern with per-client Bases.

A deliverable and report processor that extracts structured content from the documents an engagement produces, the document extraction at scale pattern.

An institutional-memory agent that remembers a client's situation, past work, and the reasoning behind it across engagements and staff changes, long-term memory scoped per client.

A decision-trace tool that captures why recommendations were made, not just what was delivered, so the firm's reasoning is queryable later, the decision trace infrastructure pattern.

Consulting problems, solved

The problems consulting builders run into are specific, and each has an answer.

Knowledge that leaves with people. This is the defining problem of the sector. Memory holds a client's context, history, and the reasoning behind past work as firm knowledge rather than individual knowledge, so when a consultant rotates off, the engagement's context stays. It's the institutional memory that doesn't depend on who's in the room.

Per-client confidentiality. One client's work and data must not surface in another's. Bases make isolation structural: each client gets a sealed environment, and an operation scoped to it can only see that client's knowledge, so confidentiality is a property of the architecture. It's the multi-tenant memory pattern.

Getting content out of deliverables. Engagements produce reports, decks, and documents in every format. Extract turns them into clean, searchable text through one API, including scans, so past work becomes a searchable asset rather than files in a folder.

Knowledge that goes stale between engagements. A client knowledge base built during one engagement is out of date by the next. Workers keep each client's knowledge current on a schedule, re-processing new material so the next team starts from an accurate picture, the self-maintaining knowledge bases pattern.

Finding the relevant past work. Deep Search finds by meaning across a client's deliverables and the firm's wider work, so a consultant can surface the relevant prior analysis even when it's phrased differently from how they searched.

The infrastructure underneath

Five primitives carry most consulting knowledge tools. Bases give per-client isolation from a single API call. Memory holds institutional knowledge that survives turnover. Extract turns deliverables and reports into searchable text. Workers keep client knowledge current between engagements. Deep Search finds past work by meaning. One API key, rather than assembling and isolating the pieces yourself.

Knowledge that stays when people go

The value of building on this compounds with every engagement and every departure, which is precisely where the do-nothing alternative loses the most. Each engagement adds to a client's knowledge base and to the firm's institutional memory, and because that knowledge lives in infrastructure rather than individual heads, it stays when people leave instead of walking out with them. Per-client isolation scales from one API call, so the firm can hold knowledge for thousands of clients without the separation getting fragile, and Workers keep each client's picture current so the next team starts informed rather than rebuilding. The firm's accumulated reasoning becomes a durable, queryable asset rather than something perpetually re-learned, which is the difference between a firm that compounds its knowledge and one that resets it with every staffing change.

Get started

Start with the getting started guide, then the use-case pages that match what you're building: self-maintaining knowledge bases, long-term memory for any agent for institutional memory, multi-tenant memory for SaaS for per-client isolation, and decision trace infrastructure for capturing reasoning. There's a free tier to build against.

FAQs

How does knowledge survive when a consultant leaves?

Because it lives in Memory and a client's knowledge base rather than in the individual's head. The client's context, past work, and the reasoning behind it are held as firm knowledge, so a departure doesn't take the engagement's context with it.

How is one client's data kept separate from another's?

Each client gets their own Base, a structurally isolated environment. Operations scoped to it can only see that client's knowledge, so confidentiality is enforced by the architecture. It's the multi-tenant SaaS pattern.

Can it extract content from past deliverables and reports?

Yes. Extract turns reports, decks, and documents into clean, searchable text through one API, handling scans as well as native formats, so past work becomes a searchable asset.

How do client knowledge bases stay current between engagements?

Workers re-process new material and keep each client's knowledge base current on a schedule, so the next team starts from an accurate picture rather than a stale one. It's the self-maintaining knowledge bases pattern.

Can the firm capture why recommendations were made, not just what?

Yes. Decision trace infrastructure captures the reasoning behind decisions, the inputs, the context, the rationale, as searchable memory, so the firm's reasoning is queryable later rather than lost.

Is this a finished knowledge tool or something I build on?

Something you build on. Exabase is the infrastructure, per-client isolation, institutional memory, extraction, and search, and you build the knowledge management tool on top, for the firm's consultants to use.