Your deal team should spend Monday on judgment, not on re-reading the data room.
Soligence builds AI for private equity firms and the companies they own: customer and deal intelligence for the firm today, portfolio monitoring and deal execution next, and value creation workstreams inside portfolio companies. Tuned to your deal process, your LP cadence, and your controls, by senior engineers who stay accountable for whether it works.
COOs, CFOs, heads of operations, and the partner sponsoring AI
Deal sourcing and diligence, IC memos, CRM and LP relationships, fundraising data requests, portfolio monitoring, fund finance and operations.
What we build for the firm →Operating partners and portco CEOs, CFOs, and COOs
The AI workstream in the 100-day plan, sponsor reporting, add-on integration, SG&A consolidation, and an EBITDA bridge the IC will believe.
What we build for portcos →Where the firm's hours go.
Lean deal teams, fee pressure, longer holds, and LPs who now ask about your AI strategy in diligence. The firm has to do more with the same people, and a large share of the hours goes to assembly rather than judgment.
The data room lands on Friday
The first read of the CIM, the QoE, and a few hundred files happens over a weekend before Monday IC. Numbers get copied between the model, the memo, and the deck, and transposed on the way.
A CRM that costs like a platform and works like a spreadsheet
Seat-licensed private-capital CRM, manually maintained, unable to tell you which LPs are most likely to fund the next deal or which relationships have gone quiet.
LP reporting and DDQs eat IR and finance every quarter
Quarterly letters, capital account statements, DDQs, and fundraising data requests rebuilt by hand from the same sources each cycle.
Portfolio monitoring is a spreadsheet
Board packs read late or not at all. Covenant headroom, KPI drift, and customer concentration found after the fact instead of flagged when they moved.
Fund finance and operations still run on email
Capital calls, distributions, waterfall checks, expense allocations, and the recurring exception hunt across the administrator, the bank, and the GP's own records.
Compliance asks where the deal data goes
Information barriers, MNPI, the marketing rule, and vendor diligence. Any AI system the firm adopts has to answer those questions before it answers any others.
Start with what is already running.
Customer and deal intelligence
The first Argus360 module in production, running at a private equity firm today. Agents do the data assembly the deal and IR teams used to do by hand, match new deals to the LPs most likely to fund them, draft deal and LP briefs, and surface signals across the portfolio: margin shifts, customer concentration, valuation comparable movement, LP sentiment.
Deal Execution and PortCo Health
Two further Argus360 modules are planned: Deal Execution (data room first read, red-flag memo with source citations, IC memo drafting) and PortCo Health (board packs read every quarter, covenant headroom and KPI drift flagged as they move). Until it ships, we scope the interim work as project work: discovery decides whether it runs inside Argus360 under license or as a system your team operates.
Everything a module does not cover
LP reporting and DDQ assembly, fund finance exception handling, research and memo production on the firm's own materials, and an AI policy framework that stays investor-ready. Built by our AI Factory, the senior engineering team and agent workflows behind every custom system we ship, and handed to your team with documentation, training, and governance.
You're running 8–15 portcos with the same operational playbook from 2019.
Every portco has its own ERP, its own reporting cadence, its own spreadsheets. Your operating team spends more time assembling data than acting on it. AI changes the operating model, but only if it's scoped to the metrics your IC actually cares about.
Monthly KPI packs take days, not hours
Your ops team manually assembles data from 3–5 different ERPs across the portfolio. By the time the board deck is ready, the numbers are already stale. Every portco reports differently.
No standard operating playbook
You know what good looks like in procurement, pricing, and back-office ops. But each portco implements it differently, on different systems, with different teams. Standardization is manual and slow.
AI pilots that stall at the demo
Some portcos tried ChatGPT or built a proof-of-concept. It never reached production because nobody scoped it to a real business metric, nobody built governance, and nobody trained the team.
SG&A that should be lower
Your portcos are paying for enterprise CRM, BI tools, project management, and documentation platforms. Most of that software is a pre-built UI over a database, something AI can replace or consolidate at a fraction of the cost.
A playbook that maps to how you actually operate.
Days 1–30: Audit and scope
We audit 2–3 priority portcos across the functions that matter most: finance/reporting, sales/CRM, back-office ops, and customer service. We identify the 3–5 highest-impact automations and scope them to specific value creation metrics.
- Cross-portco operational assessment
- Software spend audit and replacement candidates
- AI opportunity prioritization by EBITDA impact
- Board/IC-ready roadmap with timeline and metrics
Days 30–100: Build and measure
We deploy the first automation, measure impact against baseline, and document the playbook for rollout across the portfolio. Custom builds are handed to your operating team with documentation, training, and governance.
- Production AI workflow deployed in priority portco
- KPI dashboard tied to value creation plan metrics
- Repeatable playbook documented for portfolio rollout
- Team trained, governance established, handoff complete
What a 100-day AI initiative looks like for a $50M revenue portco.
An illustrative plan, not a client result: B2B services company, 200 employees, NetSuite ERP, HubSpot CRM, manual monthly reporting to the fund.
Days 1–15: Reporting automation
Owner: VP Finance + operating partner
Baseline: 4 days/month assembling board pack from NetSuite + spreadsheets
Target: Automated monthly KPI pack with variance commentary, delivered in 4 hours
Impact: CFO capacity freed for strategic analysis
Days 15–45: CRM and pipeline intelligence
Owner: Head of Sales
Baseline: CRM with low adoption, no forecast accuracy tracking
Target: AI-native pipeline intelligence with automated data capture
Impact: Sales forecast accuracy visible to the IC for the first time
Days 30–60: Customer success automation
Owner: VP Customer Success
Baseline: Renewal tracking in spreadsheets, no proactive risk visibility
Target: Automated health scoring, renewal risk alerts at 90 days, expansion signals
Impact: Improved NRR visibility and proactive retention
Days 60–100: Back-office cost reduction
Owner: COO + operating partner
Baseline: Multiple overlapping software platforms across CRM, PM, and documentation
Target: Consolidate with AI-native alternatives where ROI is clear
Impact: Measurable SG&A reduction, documented for IC reporting
The PE AI playbook
How we ramp AI at a firm and across its portfolio: discovery, the Argus360 base layer and Customer & Deal Intelligence module, further modules as needed, the AI Factory for the gaps, and a team that runs it.
Read the playbookProof, not promises
A private equity firm cut annual software spend by roughly 60% by replacing its legacy private-capital CRM with Argus360, and gained a deal-to-LP matching engine the old platform never offered.
Read the case studyTell us whether you're the firm or the portco. We'll take it from there.
Tell us about the firm or the portfolio, where the hours go today, and what you have tried. We'll tell you honestly where AI creates leverage and what a realistic timeline looks like.
Intro call
30 to 45 minutes. You describe the bottleneck; we tell you honestly whether AI is the right lever and what discovery would cover. No deck, no pitch.
Discovery
Two to four weeks. We map the workflows, data, and controls, baseline the value, and return a scoped plan with timeline and pricing. Starts at $5K, credited in full toward project work that begins within 60 days.
Project work
Argus360 for the core customer and deal intelligence, custom builds by our AI Factory for what a module does not cover. Structured the way you and we agree is best: a fixed-scope sprint, a multi-phase build, an ongoing operations partnership, or fractional AI leadership.
Most engagements follow this pattern. See how we work.