Applied AI engineering for operating companies

AI that shows up
in the P&L.

We find the workflows that move revenue, cost and cash. Then we build them into your existing systems and measure them against a baseline.

Start a conversation How an engagement runs

Work in. Value out.

Revenue: Faster quotes. Fewer invoice leaks.
Cost: Less manual handling. Less rework.
Capacity: More volume. Without more headcount.
Cash: Faster billing. Tighter collections.

Conceptual illustration · value is measured, not assumed.

Technology ecosystem · selected per engagement

Anthropic
OpenAI
Vercel
Lovable
LangChain
Braintrust
Anthropic
OpenAI
Vercel
Lovable
LangChain
Braintrust

How an engagement runs

One workflow.
A clear path to value.

Start with the people accountable for the business. Leave each stage with something they can inspect, test and own.

01 / Diagnose

Find where the value sits.

With the sponsor, CEO or CFO and the workflow owner, map the process, its exceptions and systems. Establish the baseline before deciding what to change.

Value map + baseline

Illustrative artifact

Workflow: Quote request handling
Accountable team: CEO / CFO · Sales operations owner
Process & systems: Shared inbox → price book → quote → CRM
Exceptions: Missing specifications · price overrides · nonstandard terms
Baseline: Quote turnaround · handling time · rework. Record each measure's unit, observation window, sample source and owner.
Value lens: Revenue and capacity, modeled separately from realized savings

A sample structure, not a client deliverable or a claimed result.

02 / Scope

Agree what success means.

Set the value case, acceptance criteria, controls and owners. Define the boundaries before engineering begins.

Scope + acceptance tests

Illustrative artifact

In scope: Read incoming requests, apply approved pricing rules, draft quotes and record the action
Out of scope: Changing commercial policy or accepting nonstandard terms
Owners & controls: Sales operations owns the workflow. Commercial exceptions route to the designated decision-maker.

Acceptance test	Evidence required
Source traceability	Each extracted field links to its request.
Pricing accuracy	Output reconciles to the approved price book.
Exception routing	Out-of-policy cases reach the named owner.

The value case and test thresholds are agreed for the actual workflow.

03 / Build

Engineer it into the business.

Build within existing systems. Size rules, AI and human checkpoints to the workflow's risk, then test on real cases.

Production workflow

Illustrative artifact

IntakeRead & extract
RulesValidate & price
AIDraft & explain
SystemsRecord & trace

Human checkpoints: Exceptions and higher-risk decisions route to an owner. Routine cases follow the agreed controls.
Test set: Representative requests, incomplete inputs and policy edge cases
Action record: Source · rule / model version · decision · write-back · owner
Release gate: Acceptance tests and operating controls verified before rollout

Approval is risk-based, not an automatic stop on every transaction.

04 / Pilot & measure

Measure the change, not the demo.

Roll out in a controlled scope. Compare real work with the baseline and separate the operational change from the financial result.

Results readout

Illustrative artifact

Measure	Baseline	Pilot
Quote turnaround	To establish	To measure
Handling time	To establish	To measure
Rework	To establish	To measure

Comparison: Matched case types, source records and documented exclusions
Commercial readout: Realized savings, capacity, revenue and cash reported separately
Decision: Continue, adjust or stop based on the evidence

No sample outcome numbers. The actual readout uses observed results.

05 / Operate & expand

Keep it running. Carry it forward.

Monitor, maintain and improve the workflow. Use what holds up in operation to guide the next workflow and the next portfolio company.

Runbook + roadmap

Illustrative artifact

Operating owner: Named workflow owner, engineering support and escalation route
Monitor: Quality · exceptions · system failures · usage cost · business measures
Incident path: Contain the issue, restore the agreed fallback, notify the owner
Change control: Test changes against real cases before release
Next workflow: Prioritize from the value map and operating evidence
Portfolio expansion: Reconfirm systems, owners, controls and the baseline at each company

Scope and support responsibilities are defined in the engagement.

Illustrative artifacts. No client data, outcome claims or standard timeline. Each engagement is scoped to the business.

Explore AI Transformation

Two practices, one engine

Build from what
the business already has.

Our primary practice

AI
Transformation

Rebuild the workflows that run the company. Diagnose, build, prove and operate.

Explore the practice Data licensing

Data
Partnerships

We license operational data from companies and supply it to AI developers. Owners pay nothing.

Explore the practice

Commercial judgment / Engineering execution

The value case and
the working system.
One team.

Know what's worth building.: Start with revenue, cost, capacity and cash. A technically interesting project is not enough; the business case has to hold.
Own the work beyond the demo.: Integrations, controls, acceptance tests and ongoing operation. The workflow has to work where the company actually runs.

For private equity

One team.
A portfolio of possibilities.

Your portfolio's applied AI engineering team. Sponsor-level terms, a baseline and owner for every workflow, and playbooks that carry from one company to the next.

Discuss your portfolio

CloseOrder to cashQuotingReports Co. A Co. B Co. C Co. D

Illustrative: one workflow at one company, then across the portfolio

Data Partnerships

Data licensed.
Terms agreed before it moves.

We license operational data from companies and supply it to AI developers. Owners pay nothing and are paid from licensing proceeds, with their payout agreed in writing before any data is shared or supplied. Rights and permissions are confirmed before supply. AI Transformation work never gives us any right to license or supply your data; data licensing is a separate decision under a separate written agreement.

How data licensing works

Start with
one workflow.

Tell us where revenue, cost or cash is getting stuck. We'll tell you what's worth building, and what isn't.

Start a conversation Data Partnerships inquiry

AI that shows up in the P&L.

One workflow.A clear path to value.

Find where the value sits.

Value map + baseline

Agree what success means.

Scope + acceptance tests

Engineer it into the business.

Production workflow

Measure the change, not the demo.

Results readout

Keep it running. Carry it forward.

Runbook + roadmap

Build from whatthe business already has.

AI Transformation

Data Partnerships

The value case andthe working system.One team.

One team.A portfolio of possibilities.

Data licensed.Terms agreed before it moves.

Start withone workflow.

AI that shows up
in the P&L.

One workflow.
A clear path to value.

Build from what
the business already has.

AI
Transformation

Data
Partnerships

The value case and
the working system.
One team.

One team.
A portfolio of possibilities.

Data licensed.
Terms agreed before it moves.

Start with
one workflow.