AI Transformation without the slop.

• Measurability first, and the baseline is observed

  Before anything gets automated, we measure what the current way of working actually costs you: the time it takes, the handoffs it needs, and the errors that surface late. Not from a survey or a workshop, but from how the work already runs. A starting point you can verify is what makes the improvement believable later, to you and to anyone you answer to.

• Provenance, or it does not ship

  You should always be able to ask where a number or a decision came from and get a straight answer. The systems we build show their work. Every output traces back to its source, every step can be checked, and when the system is not confident it says so and hands off to a person rather than guessing. Output that looks authoritative but cannot be traced is not an asset. It is a liability.

• Your workflow is the specification, not our template

  The way your team already works, the artifacts they produce and the shape of the final deliverable, is the specification we build to. We fit the system to your process instead of bending your process around a tool. Done well, the change feels like the work got faster, not like everything got replaced.

• Orchestration is the hard part, and it is the point

  Real work takes more than a clever prompt. It takes the right models and tools wired together, memory of what happened before, and enough context to choose the next step correctly, run after run, without drifting. Coordinating agents, tools, memory and context is the discipline behind the name, and it is where most AI efforts quietly come apart.

The relevant question is not where the experience happened. It is whether the person designing your system has run into its failure points before. Most of them by hand, years before they could be automated.

The long version, with all forty-six skills mapped across the three layers, is at ericwoo.ai.

Knowing that a number is wrong takes knowing the domain. That only holds where the experience is real, so the list stays tight.

• Investment firms & allocators

  Family offices, funds of funds and institutional allocators carrying document-heavy back offices: capital account statements, fund financials, schedules of investments, capital calls and distributions. Reconciliation is the product, not a feature of it.

• Venture capital firms

  GPs preparing for institutional LPs, and firms whose portfolio monitoring is still rebuilt in a spreadsheet every quarter.

• Startups, seed through Series A

  Founders who need investor narrative, data-room architecture and reporting that survives diligence, well before they can justify hiring for it.

• Small businesses

  Restaurants, service firms and owner-operated businesses. The least-served segment and often the highest measurable return, because the current baseline is entirely manual. No prior AI fluency assumed or required.

• Every engagement is quoted per project. No hourly rates, and no billing by the increment. You know the number before work starts.
• No minimum and no maximum. A one-week diagnostic and a multi-phase build are both real engagements.
• Scoping is priced on its own, and comes first. It produces the firm estimate for everything downstream, so you are never asked to commit to a figure nobody can yet defend.
• Advisory-heavy. This is not an outsourced or on-demand Chief AI Officer arrangement. What you get is judgment, architecture and working systems, not a seat filled on an org chart.
• Two exits, named up front. At conclusion you choose: a full documented handover so your team or a third party can run it, or a continuing maintenance arrangement. Either way the migration checklist is complete before we are done.