How this is different from AI consulting
Most AI consulting sells implementation capacity. We sell a control system that already exists, installed by the people who built it.
The product is the jumpstart
Engagements do not start at a blank page. They start with OloLand already running on your deal — evidence graph, deterministic engines, assumption register, audit trail. We configure and extend from there instead of rebuilding diligence infrastructure you can buy.
Forward-deployed, not advisory
No strategy decks and no maturity matrices. An engineer works inside your stack, on your real deal, alongside your team, and leaves behind a workflow your analysts run without us.
Agent assurance is the deliverable
Standing up an agent is the easy half. The half that survives an investment committee is showing what the agent relied on, which assumptions it made, where it was corrected, and who approved it. That control layer is what we install.
Three kinds of engagement
Each one ends with a workflow running in your environment on the OloLand platform.
Diligence workflow build
Your diligence process, running as an agent workflow on a live deal — document intake, source-linked findings, risk matrix, and a cited memo section your IC can trace back to the underlying documents.
Outcome: A working workflow on a real deal, owned by your team
Agent assurance install
You already have agents. We add the control system around the diligence work that runs through OloLand: evidence provenance, assumption tracking with owners and status, approval gates, inspectable run history, and a reconciliation path when two documents disagree. Extending assurance to agents you host separately is an integration build, and we scope it as engineering rather than folding it into setup.
Outcome: An auditable record behind agent output
Rail and integration work
Connecting OloLand to the surfaces your team already works in — the audited MCP rail, agent clients, data rooms, and internal systems — so diligence work happens where the deal team already is.
Outcome: OloLand available inside your existing tools
Private equity firms looking for packaged offerings and published fee bands should start at the AI Value Creation Studio. Lower-middle-market firms wrestling with generic AI output, junior over-reliance, and untapped firm knowledge should read AI Consulting for Lower-Middle-Market PE.
How we work
Scoping call
We look at one real workflow and decide together whether this is an engagement, a subscription, or nothing. If the product alone solves it, we say so.
Fixed-fee scope
Written scope with defined milestones and a fixed fee. Never time-and-materials, and never open-ended discovery billed by the hour.
Build and hand back
We build against your real documents and deals, then hand the workflow to your team with the platform underneath it. The engagement ends; the capability stays.
Who this is for
We keep a narrow scope on purpose, and the test is scope rather than job title. Diligence, underwriting, and the control system around them — whether the buyer sits in a deal team or the office of the CFO. Not general-purpose AI delivery.
Good fit
- Private equity firms and independent sponsors running repeat diligence
- Teams already piloting agents that now need an audit trail behind the output
- The office of the CFO — for close and reporting integrity, forensic and reconciliation work, assumption and approval governance, and the audit trail behind finance agents. These engagements anchor to a live underwriting, diligence, or control workflow rather than to an acquisition
- Firms that want the workflow embedded in their stack, not another platform migration
- Deal teams whose IC or LPs ask "where did this number come from?"
Not a fit
- Generic AI strategy engagements with no diligence or underwriting workflow attached
- Trading, alpha research, or portfolio-construction workflows — outside what we build
- Treasury operations and general finance-function AI implementation, including for CFO buyers
- Prompt-engineering workshops and chatbot builds
- Work that requires us to turn off the verification layer
We build on the rails you already chose
OloLand is a member of the Claude Partner Network, and the platform runs on both Anthropic and Google model rails in production. Reasoning engines are replaceable components in our architecture — the evidence graph, deterministic engines, and audit record are not. That means an engagement does not lock your firm to a model vendor, and it does not ask you to migrate off the tools your team already uses.
Neutrality shapes deployment too. Where a mandate restricts where deal documents may sit, the Pre-LOI forensic screen and QoE batch can run unattended on a self-hosted appliance on the Claude Platform rail: document files, code execution, and network egress stay on infrastructure you control, while model reasoning still flows through Anthropic’s control plane. Those two workloads are the boundary as it stands today — interactive deal chat is not yet self-hostable. We would rather you read that here than find it in an architecture review.
We do not resell model capacity or seats, and we take no margin on your model spend. We are paid to deliver a working, auditable workflow on the contracts you already hold.
Common questions
What is OloLand AI Consulting?
It is a forward-deployed engineering engagement: an engineer embeds with your deal team and builds an agentic diligence or underwriting workflow on your real deal, inside your own stack. Engagements start from the OloLand platform rather than a blank page, so the work begins with evidence graph, deterministic financial engines, assumption register, and audit trail already running.
How is it different from a traditional AI consulting engagement?
Traditional AI consulting sells implementation capacity and usually starts from scratch. OloLand ships a diligence control system as a product; consulting configures and extends it. The deliverable is a working workflow your team owns and runs without us, not a strategy deck or a maturity assessment.
What is agent assurance?
Agent assurance is the control layer that makes AI agent output defensible: source-linked evidence, explicit assumptions with owners and status, approval gates, inspectable run history, and flagged conflicts when two documents disagree rather than silent averaging. It is what lets a deal team answer "where did this number come from?" in front of an investment committee.
Does agent assurance cover agents running outside OloLand?
The control system applies to work that runs through the OloLand platform, and that is the scope we quote against by default. Extending assurance to agents your firm hosts separately is an integration build rather than configuration, so we scope and price it as engineering work in the engagement rather than treating it as included setup.
Who is OloLand AI Consulting for?
Private equity firms and independent sponsors running repeat diligence, teams already piloting AI agents that now need an audit trail behind the output, and the office of the CFO on the same narrow scope rather than finance transformation generally. It is not for trading or alpha-research workflows, generic AI strategy work, or prompt-engineering workshops.
What kind of CFO-office work do you take?
The same work we do for deal teams, sold to a different title: close and reporting integrity, forensic and reconciliation workflows, assumption and approval governance, and the audit trail behind finance agents. The test is scope, not job title — if the work is diligence, underwriting, or the control system around them, it fits, and it does not need to sit on an acquisition. A CFO engagement anchors to a live underwriting, diligence, or control workflow instead of to a deal. Treasury operations, trading, alpha research, portfolio construction, and general finance-function AI implementation do not fit, and we will say so rather than take the engagement.
How are engagements priced?
Fixed-fee and milestone-based, never time-and-materials. Scoping starts with one real workflow, and if the OloLand product alone solves the problem we say so rather than selling an engagement. Packaged private-equity offerings with published fee bands are listed on the AI Value Creation Studio page.
Which AI models does OloLand build on?
OloLand is a member of the Claude Partner Network and the platform runs on both Anthropic and Google model rails in production. Reasoning engines are replaceable components in the architecture; the evidence graph, deterministic engines, and audit record are not. Engagements do not lock a firm to a single model vendor, and OloLand does not resell model capacity or seats.
Start with one real workflow
Tell us the deal workflow that costs your team the most time, and we will tell you whether it needs an engagement or just the platform.
For boutique diligence and fractional-CFO firms, see the Diligence Desk pilot.