From AI access to an AI operating model.
Free up to 20% more firm capacity to increase revenue without adding headcount in a 4-week sprint. If we don't deliver, we keep working until we do. At our cost.
Automate the machinery. Preserve the judgment.
AI was supposed to make things easier. Right now, everyone's just doing it alone.
For most firms, using AI still means opening a chat window, typing a prompt, and waiting for an answer — one person, one conversation, on their own time, with their own prompts, at their own skill level. There's no shared standard, no shared context, and no one accountable for whether any of it works. It doesn't run in the background. It isn't built into how work moves through the firm. People reach for it alone, not as a system the whole team runs on.
The relationship you let go cold
You placed someone excellent. They're doing well, they trust you, they'd refer you a mandate without thinking twice. Nobody owns following up — so eighteen months later you find out on LinkedIn that they've moved into a hiring role, with no idea if you even crossed their mind. One relationship, kept warm, can cover the cost of an engagement like this. Instead, it just goes cold.
The reconstruction scramble
A client update's due in two hours, and you're not writing it — you're doing archaeology. Was there something in an email that never made it into the CRM? What do you personally remember that isn't written down anywhere? Every hour spent piecing together what should already be organized is an hour not spent running the next search.
The conviction that never made the page
A partner gets off six calls and doesn't have time to write notes on any of them. So the candidate summary gets built from a resume and one line — “strong, light on technical depth” — and it reads like every other summary the client could've gotten anywhere. The partner felt real conviction on that call. None of it made it to the page. The client can't feel conviction they never got to read, so they hesitate, because the case for them never landed.
The question that shouldn't stop the search
You go to check the comp range, or whether the role's remote, or the bonus structure — basics that should already be written down — and it's nowhere. So you interrupt a partner mid-search to ask something that has nothing to do with their judgment. Multiply that across a search, and every interruption is a delay stitched into the timeline.
Why what you've tried hasn't worked.
You've probably already tried the obvious fix: give the team access, encourage them to use it, see what happens. It gets picked up by whoever has bandwidth that week — and the moment a live search needs their full attention, which is most weeks, the AI experiment is the first thing that stops. Adoption stays uneven. Nothing compounds. Six months later you have the same tool and the same gap.
Four ways freed-up time turns into more mandates.
Give partners their BD hours back, and BD activity goes up. More outreach, more conversations, more mandates from the same book of relationships.
Free up delivery time, and the same team can run more searches at once — without adding headcount or stretching quality.
A cleaner, better-supported process lifts fill rate. The same pipeline turns into more completed placements.
Shorter time-to-shortlist means the slot turns over faster — protecting the fee against a search that drags and stalls.
These aren't abstractions. In the diagnostic, we put your firm's actual numbers against each lever so you can see where your return is largest before we build anything.
From AI access to an AI strategy — in 4 weeks.
One diagnostic sprint. One enablement sprint. Then optional ongoing support.
Diagnose
We trace each friction point back to the lever it's costing you — origination, capacity, conversion, speed — and the specific workflow that closes the gap. You leave with a sprint delivery plan: what gets built, and when, prioritized to where your return is largest.
Redesign
We design the future-state workflow: what gets eliminated, standardized, templated, AI-enabled, or intentionally kept human. Built around how your firm actually runs searches, not technology for its own sake.
Build
We implement the priority systems: an AI knowledge environment loaded with your firm's context and voice, workflow automation, integrations, reusable templates, and reporting infrastructure.
Embed
We train the team on their actual mandates, document the new operating model, and support rollout until it's actually how the firm works — not a system nobody opened twice.
Maintain + Optimize
We can stay involved fractionally — monitoring systems, updating knowledge and workflows, and identifying the next opportunity as the firm's needs shift.
"We put a number on the outcome of the enablement — agreed together at the end of the diagnostic, measured on the workflows we rebuild. If we don't deliver, we keep working until we do. At our cost."
The work that quietly eats your best hours.
Where it starts depends on your firm — the diagnostic maps yours.
Business operations, technology delivery, and AI engineering.
Together we bridge business operations, technology delivery, and AI engineering — from identifying the right problem through to putting a working solution into the hands of your team.
Leads business diagnosis, workflow redesign, use-case prioritization, implementation strategy, and organizational adoption. Oana spent two and a half years inside an executive search firm, rebuilding its operating layer as it scaled from seven to twenty people: eight workflow automations that cut production time by 50–85%, an internal AI knowledge assistant, and systems that replaced manual handoffs — without adding operations headcount. Before that, six years in business intelligence and strategy at two of Canada's largest law firms.
Leads technical delivery, architecture, and implementation across systems and integrations. 15+ years of enterprise technology leadership across financial services, healthcare, and SaaS, including a $120M core banking transformation and budgets up to $120M, teams of 400+.
Provides advanced AI engineering expertise for use cases requiring deeper technical development. ML engineer and PhD candidate in Artificial Intelligence, specializing in LLMs, RAG pipelines, knowledge graphs, and custom NLP systems.
Start with a diagnostic.
Tell us where the work is getting stuck, and we'll show you what a leaner, AI-enabled operating model looks like for your firm — with one quick win built before the diagnostic ends.