Data migration
Post-M&A data migration
Unify messy customer, contract, vendor, and financial data after M&A.
AI-native data migration, and transformation.
What we do
We transform messy data and manual workflows into AI-native software.
Pipelines
Document & data pipelines
Turn documents and exports into structured, cited data in your system.
Custom software
Custom operational apps
Rebuild paper and spreadsheet workflows using software.
Client coverage
The firms that run private equity trust us with the work.
H.I.G.
CAPITAL
Mark Fiske
Head of Digital Transformation
AI only creates value when it vanishes into the workflow. Prosights is one of our few partners that delivers on this promise.
Apax˟
Andrew Morgan
AI Delivery Lead
Prosights handles our manual work directly in our own formats — delivering a real step-change in our team's productivity.
More than 70% of the 10 largest PE firms have used Prosights. Weighted by assets under management, they hold roughly 70% of the group's combined capital.
How it works
We solve your highest-value problem using your own data before you commit.
01.Start with a working proof
A free consultation and a working prototype on your real data. No commitment until you've seen it run.
02.Define the path to value
We sequence the work by return — highest-ROI first — into milestones that each stand on their own.
03.Build within your existing stack
We build inside the systems you already run — your VPC, your warehouse, your systems of record.
04.Launch, learn, and scale
You go live fast, then we refine and redeploy the pattern to the next workflow — value compounding.
96 hours
First working prototype
~4 weeks
Live in production
Zero
Added headcount
100%
You own your IP
case studies
Enabling the next generation of PE-backed portfolio companies.
$300K+
billing leakage recovered
~40%
faster unit throughput
CASE 1
Read morePaperless Field Workflow
A machinery and equipment services company — repairing, inspecting and overhauling units across more than a dozen facilities — ran every job on a paper file that physically traveled with the unit through roughly ten work centers. Hand-offs were manual, asset IDs were transcribed by hand, completed labor and materials often went uncaptured and unbilled, and neither customers nor managers could see where a unit was.
10 weeks
total implementation
6 divisions
firmwide adoption
CASE 2
Read moreEmbedded Extraction Platform
The firm runs its own internal AI platform, built in-house with multiple AI vendors side by side. It wanted production-grade document extraction — board decks, P&Ls, LP agreements and fund-administration documents into structured, template-mapped Excel — available across the firm inside that platform, not as another standalone tool.
~95%
steady-state auto-approval
~200 hours
reviewer time returned / close
CASE 3
Read moreAI-Native Bookkeeping
A global corporate-services and fund-administration firm ran outsourced bookkeeping for thousands of end-clients across multiple countries — each invoice keyed by hand and coded to that client's own chart of accounts, across several accounting systems. The work was expensive to scale, and every reviewer's judgment evaporated after the close, so each similar invoice became a fresh review task.
$5M+
revenue leakage recovered
3 month
saved vs a manual migration
CASE 4
Read morePost-M&A Data Migration
A PE-owned Nordic software company had grown through repeated acquisitions — inheriting ~700 customers whose contracts lived across five formats and four languages, in scanned PDFs and handwriting. A prior external migration loaded the data by how it was invoiced rather than what the agreements said, so ~90% of legacy contract dates were wrong, quote-to-cash was breaking, and no link tied CRM records to the signed agreements that governed them.
2 weeks → 1 hour
of RFQ research
~40%
faster first quotes
CASE 5
Read moreEngineering Reuse Discovery
An equipment manufacturer (~$400M revenue) builds highly customized hardware, but many subcomponents — its "platform technologies" — are reusable. Reuse ran at 60–70% within a business unit and far lower across them, because nobody could systematically search what already existed; Sales and Engineering relied on individual memory. A first quote for a new RFQ took two weeks to three months, as engineers re-developed components the company already owned — and time-critical tenders were being lost to faster competitors.
get started
Start with an AI audit.
A short working session with our team to find the highest-value workflow in your operation. You leave with a concrete plan — yours to keep, whether you build it with us or not.
FAQsHave questions?
Have questions?
Find answers.
Common questions from PE firms and portfolio companies exploring a transformation.
What happens during the AI audit?
A working session where we map how a workflow actually runs today — where the data lives, what systems it touches, and what’s blocking it. You leave with a concrete plan for the highest-value workflow to transform.
How much involvement is required from our team?
Very little. Your team provides context on the workflow and systems involved. We handle the design, build, and deployment, coordinating with the right technical and operational stakeholders along the way.
How long does it take to go live?
A working prototype in as little as 96 hours, and typically live in production within a few weeks — depending on the complexity of the workflow and the systems involved.
How do you handle security and governance?
We deploy inside your environment — your VPC, your warehouse, your systems of record — operating within your existing permissions and security policies. Nothing leaves your stack, and all actions are logged for audit.
Do we have to migrate off our existing systems?
No. We transform data and build inside the stack you already run — ERPs, CRMs, data warehouses — through APIs and integration layers. No rip-and-replace.
Is everything built bespoke to us?
Yes. Every engagement is designed around your specific workflows, systems, and operational rules. We reuse patterns we’ve proven elsewhere, but what we build is fit to your operation.
What about our existing AI initiatives?
We’re upstream of them. Whatever copilot, agent, or model your team is building works better on a clean foundation — which is exactly what we deliver.
How does pricing work?
Per engagement, scoped to the outcome we’re delivering — typically priced against the headcount or system-integrator cost it replaces. Discussed after the audit, once we understand the requirements.