Production-grade AI for the firms whose product is their people.
We help boutique and mid-market consulting firms put AI to work inside the firm: automating delivery, compounding institutional knowledge, and standing up AI offerings you can take to your own clients.
AI that runs inside the engagement, not beside it.
The architecture sits on the firm’s own knowledge, plugs into the tools your teams already deliver in, and is governed so partners can stand behind every output.
Knowledge ingest
Past decks, models, memos, transcripts and methodology docs are indexed into a single retrievable corpus: the institutional knowledge that usually walks out the door.
The state of AI in consulting firms.
Firms have piloted AI everywhere (a chatbot here, a research assistant there), but few have it running through real delivery. Consulting sits in a higher-trust-bar quadrant than most software buyers, for structural reasons.
When the deliverable is advice, an output that reads well but reasons wrong is worse than no output. The accuracy bar is the firm’s reputation.
A firm’s real asset lives in old decks, models and partner heads, unindexed and lost between engagements. AI is only as good as the corpus it sits on.
Your clients are asking what your AI offering is. The firms that can answer with something real (built, not slideware) win the next mandate.
The opportunity is real. The trust bar is higher.
What goes wrong with AI in consulting firms.
Four failure patterns we see again and again, and what they require to fix at the engineering layer.
Pilots that never reach delivery
Most firms have a sanctioned chatbot nobody uses on a real engagement. A tool that lives outside the delivery workflow never compounds; it stays a demo.
We build into the workflow (slides, docs, the project workspace) so AI is where the work already happens.
Confident, wrong, unsourced
A generic model invents a statistic in a client deck and your credibility is gone. Without retrieval grounded in your own material, fluent output hides quiet errors.
Retrieval over the firm’s corpus, citations on every claim, and evaluation that catches hallucination before a partner sees it.
Knowledge that walks out the door
Your best methodology lives in scattered decks and a few partners’ heads. When they leave or get busy, the firm relearns what it already knew, on the client’s clock.
We treat knowledge capture as core engineering: indexing, permissions, schema, and a corpus that compounds.
Quality that degrades silently
A tool that worked at launch slowly drifts as models update and content grows. Without continuous evaluation, quality erodes invisibly and analysts quietly stop trusting it.
Eval + regression in CI, quality dashboards, and alerts when output drifts below the bar.
What makes consulting AI different.
Three constraints that consulting AI has to be designed around, not retrofitted into.
Two engagement paths, depending on where you are.
We work differently with firms just starting their AI journey versus firms scaling what’s already running.
For firms that haven’t yet deployed AI inside delivery.
We map where AI delivers the most leverage across your practice, design the knowledge and data foundations correctly from day one, and ship the first deployment into a real engagement, with measurement built in.
First AI shipped into delivery, with measurement baked in.
For firms already running AI across the practice.
We audit what’s running, find the highest-leverage wins (quality, cost, adoption, reliability) and implement with quantified before/after, so AI scales from a few power users to the whole firm.
Higher quality, lower cost, firm-wide adoption. Measured.
We don’t just plan it; we ship it.
A measured outcome from a production-AI engagement, the same operational discipline we bring to consulting firms.
The trust bar is higher here. So is ours.
Before AI was the conversation, we were building performance-critical, real-time systems for Disney, Coca-Cola, Apple, LEGO, Warner Bros, and SXSW, systems that had to run reliably under unpredictable load, integrate with existing infrastructure, and hold up at brand scale.
That same engineering discipline (integration rigor, provenance, real SLAs, and quality you can stand behind) is what production AI requires. Especially in a consulting firm, where the deliverable is judgment and your reputation is on every page.
