Internal knowledge retrieval
Make approved past work, methods, templates, research, and delivery assets easier to find with permissions and source-backed answers.
Professional services
AI for professional services firms that sell expertise, not generic output.
For consulting firms, agencies, advisory teams, and expert organizations, we turn past work, research habits, proposal language, and delivery methods into reviewed workflows the firm still owns.
Focus
expertise · proposals · delivery
Risk lens
client context · quality · review
Output
reusable knowledge systems
Where AI helps an expert firm
Expert firms run on context: past work, client nuance, delivery methods, research patterns, proposal language, and the judgment of senior people. The opportunity is to make that knowledge travel farther without flattening into something generic.
The strongest use cases usually start inside the firm: finding prior work, preparing research, drafting proposal components, summarizing client context, building reusable delivery assets, and creating internal tools that make teams more consistent.
Services that fit expert firms
LLM, RAG, and evaluation systemsDesign internal knowledge retrieval, source-backed answers, evaluation sets, and quality reporting.AI agents and automationBuild proposal, research, delivery, reporting, and review workflows around human approval.Fractional AI leadershipProvide senior guidance while the firm defines its AI operating model, tooling, and internal ownership.
Use cases
Workflows where expertise should travel farther.
Proposal and pitch support
Assemble relevant examples, draft sections, tailor language, and check requirements while keeping final judgment with the owner.
Research and synthesis workflows
Turn source material into structured briefs, open questions, assumptions, and reviewer-ready summaries.
Delivery operations
Create repeatable workflows for status reporting, meeting summaries, client documentation, QA checklists, and handoff materials.
What to protect
The risks worth taking seriously: client confidentiality, client-specific context, unapproved reuse of past work, and model-generated claims that sound polished without being grounded. AI should support a firm's expertise; it should not blur where that expertise came from.
We help firms define the operating model around AI: which sources are approved, who can use what, where review is mandatory, how quality is measured, and what the handoff looks like for internal owners.
Answers
Questions firm leaders usually ask first.
Where does AI belong first?
Internal expertise reuse, past-work retrieval, research preparation, proposal drafting, delivery workflows, and senior knowledge capture are usually better first moves than client-facing automation.
What should firms avoid?
Avoid generic rollouts that expose confidential client context, flatten expert judgment, or create client-facing work without review and source checks.
Is RAG useful here?
Yes, when the knowledge base is built around approved sources, permissions, reusable patterns, and retrieval evaluation.
What should be in hand at the end?
A roadmap, knowledge architecture, proposal or delivery workflow, evaluation set, automation plan, governance notes, and handoff documentation.
Next step
Have firm knowledge that should become easier to use?
Start with the proposal, research, delivery, or knowledge workflow that keeps getting rebuilt by hand.