Two theaters, one firm.
We deploy inside portfolio companies and inside the sponsor itself. The same engineering discipline — forward-deployed, model-neutral, eval-driven — applied to both sides of the table.
Portfolio companies.
Six practices inside the operating company — where frontier models earn their keep against the KPIs the sponsor already tracks.
The oldest levers in a PE playbook, now redrawn by models that can read a bill of lading and a contract in the same pass. We rebuild forecasting, inventory positioning, procurement, and exception handling around systems that surface decisions rather than dashboards. The work is concrete and the KPIs are the ones the CFO already tracks.
Pipelines are the obvious target and the one most engagements get wrong. The value is not in writing better outbound copy; it is in re-architecting how a portco qualifies, routes, and prices an inbound request against the specific contours of its vertical. We build the supporting infrastructure first and the agent-style interfaces second.
Close cycles, reconciliation, vendor onboarding, audit preparation — the mechanical work that sets the pace of every portco month-end. Frontier models handle these loops reliably when the surrounding pipeline is built properly. We build the pipeline first and let the models do the work they are actually good at.
Contact centers, self-serve portals, and the long tail of account management. The work is not about replacing humans; it is about moving the boundary between a supervised response and an unsupervised one deliberately, with evaluation pipelines that make the boundary visible to the operating committee.
Underwriting memos, research notes, investment committee packs, engineering reviews, policy and claims packets — the knowledge work that a portco's senior people spend half their week producing. We target the loops where the format is stable, the inputs are machine-readable, and the reviewer is still a human with a pen.
Model-assisted review of contracts, policies, filings, and transaction data. This is the practice area with the sharpest regulatory edges and the one where model neutrality matters most — the regulated entity cannot afford to be locked into a single vendor when the supervisor asks a pointed question in eighteen months.
The sponsor itself.
Every function inside a PE firm is knowledge work performed by expensive humans reading documents and producing documents. The entire operation is LLM-shaped. We deploy inside the firm — deal team, finance, IR, legal — with the same engineering discipline.
Associates screen hundreds of CIMs and broker teasers each year. Frontier models read all of them in hours, score each against the fund's investment criteria, surface the twenty that matter, and draft preliminary memos with citations to the source material. What took a team a week takes a pipeline an afternoon.
The data room is the most document-intensive phase of any deal — thousands of contracts, financial statements, org charts, customer data, and legal filings, read under time pressure by expensive humans. AI reads the entire room in hours, flags risks, extracts key terms from every contract, cross-references customer concentration, and drafts the diligence tracker automatically. The deal team reviews exceptions, not documents.
LBO models, sensitivity tables, scenario analysis, returns waterfalls — the mechanical core of deal execution. AI builds the first-pass model from the CIM and data room outputs, runs fifty scenarios instead of five, audits formulas, and generates sensitivity tables. IC memos that take two to three weeks of prose synthesis come back as structured first drafts in forty-eight hours. The team adds judgment, not formatting.
Operating teams manually collect monthly KPIs from thirty to eighty portfolio companies via email and spreadsheet, build dashboards, and write operating committee memos. AI agents pull data directly from portco systems or standardized reporting templates, flag variances against plan, generate the operating committee memo with commentary, and build the dashboard. The operating partner reads a finished analysis, not raw data.
Quarterly letters, DDQs, annual meeting materials, capital call notices, performance attribution — the IR function is almost entirely document production against structured data. The quarterly letter drafts itself from fund performance data. The DDQ engine maps your canonical answers to every new questionnaire format, flagging only the questions that need fresh human input. Annual meeting decks assemble automatically from quarterly outputs.
NAV calculations, waterfall computations, management fee tracking, carried interest allocation, audit prep, side letter tracking, regulatory filings. The mechanical backbone of fund operations, performed by senior professionals whose time is better spent on judgment calls. AI runs the waterfall, reconciles NAV, prepares audit workpapers, tracks side letter obligations across LPs, drafts Form PF and ADV filings from structured data, and flags discrepancies before the CFO or GC sees them.