Primary outcome
make institutional knowledge searchable and actionable
What we ship
knowledge graph, retrieval assistant, content governance, and freshness workflow
KPIs we report on
search success, time saved, knowledge freshness, and repeated question reduction
What "automating knowledge management with AI" actually means
Automating knowledge management with AI is not a single product you buy. It is a workflow you redesign around AI as the operating layer. The agent handles the high-volume, high-structure tasks. Humans handle edge cases, exceptions, and trust-sensitive decisions. The system is instrumented to measure search success, time saved, knowledge freshness, and repeated question reduction and improve weekly.
What changes by industry is the systems the agent integrates with, the data it retrieves over, the controls it operates under, and the KPIs it has to defend. The architecture is similar; the integration and the controls are different.
The architecture we use for AI knowledge management
- Frontier LLM — Claude, GPT-4-class, or Gemini. We benchmark candidates on a labelled test set during Discovery.
- Retrieval layer over your approved internal sources, with source citations on every output.
- Tool use for reads and writes against your operational stack (CRM, ERP, ticketing, data warehouse).
- Reviewer queue for low-confidence outputs. Confidence thresholds set per workflow.
- Evaluation harness — labelled test set, weekly accuracy reports, regression alerts.
- Versioned prompts and reviewer-action audit logs for traceability.
11 industries with a scoped engagement page for knowledge management. Each is a dedicated build with industry-specific systems, controls, and pricing.
How do you automate knowledge management with AI?+
We map your existing knowledge management workflow, identify high-volume and high-structure tasks, build an AI agent that handles those tasks, and route low-confidence cases to a human reviewer. The build connects to the systems your industry already runs on, runs against a labelled test set, and ships behind a reviewer queue before it sees production traffic. We measure search success, time saved, knowledge freshness, and repeated question reduction from day one and improve weekly.
What is the best AI agent for knowledge management?+
There is no single off-the-shelf "best" agent for knowledge management — the right architecture depends on the systems and data of your industry. We typically combine a frontier LLM (Claude, GPT-4-class, or Gemini) with a retrieval layer over your approved sources, tool-use for your stack, and a reviewer queue. We benchmark candidates against a labelled test set during Discovery and pick the model with the best accuracy/cost ratio.
What does AI knowledge management cost?+
Three phases, billed separately. Discovery sprint: $6k. Build engagement: $22k–$30k. Run retainer: $3k–$5k / mo. ~$34k–$60k typical year 1 (60% take the run option for ~6 months). Pricing varies slightly by industry — see the industry-specific pages below.
How long does it take to deploy AI knowledge management?+
Thin-slice in production in ~6 weeks after Discovery, full Build phase over 7-10 weeks. By day 90, search success, time saved, knowledge freshness, and repeated question reduction is instrumented and you have a baseline against which to expand to adjacent workflows.
Which industries do you build AI knowledge management for?+
11 industries listed below have a scoped engagement page for knowledge management, each with industry-specific systems, controls, and KPIs. Common starting industries include Banking, Healthcare Providers, Pharmaceuticals, Biotechnology, Construction, and others. Don't see yours? We build for any sector — tell us about your workflow and we'll scope it.
What do we own, and what do you own?+
We own workflow design, prompts, retrieval architecture, evaluation harness, and weekly improvement. You own data access, policy, exception approval, and final commercial decisions. At the end of the engagement, every prompt, eval, and config is handed over — no lock-in.
Selected portfolio
Real builds tied to knowledge management
A rotating selection of engagements where knowledge management was a primary driver, drawn from our active portfolio. Sectors and scope are accurate; client identities are withheld under engagement NDAs.
Q4 2025
Internal automation tool — workflow automation for consulting operations
Multi-vertical consulting group · Europe
Internal automation tool to streamline workflows, reduce manual administrative load, and improve operational efficiency across consulting and management processes. Integrates with existing systems rather than replacing them, automating handoffs and document flows that previously moved through email.
- Workflow automation engine
- Document-flow integration
- Operational dashboards
Q3 2025
Radiology workflow application — case handling and reporting
Medical imaging operator · Europe
Application supporting radiology workflow: case intake, structured reporting, document handling, and quality-assurance loop. Designed for regulated medical-imaging context with audit trail and role-based access.
- Web app + secure storage
- Structured reporting
- Audit-trail compliance
Q1 2026
Premium bilingual corporate site + internal CRM
Multi-vertical consulting group · Europe
Corporate marketing site with animated bento-grid editorial, bilingual content architecture, and an internal CRM behind the scenes for lead handling. Designed to project a premium positioning aligned with enterprise buyers while keeping marketing-team ownership of the content layer.
- Next.js + animated bento grids
- Bilingual content layer
- Internal CRM integration
Client identities withheld under engagement NDAs. Sector, geography, and scope are accurate. Full case studies on request.