AI-native services for private credit. Senior credit expertise, encoded into AI workflows your analysts consume without learning AI tools.
Published by Accretive AI. Last updated: April 2026.
What Accretive AI Does
Accretive AI is an AI-native services firm that builds and maintains AI-driven workflows for private credit and asset-based lending fund managers. We are not a software platform. We don't sell subscriptions, user seats, or a product your analysts have to learn. We operate as an embedded credit-AI team: we build the workflows, configure the borrower-specific infrastructure, maintain the systems over time, and deliver finished outputs that your team consumes directly — quarterly monitoring packages, borrowing base exception reports, prescreen and IC memos, credit model updates, variance analysis.
Our engagements target the workflows that consume the most analyst time in private credit funds: portfolio monitoring, new deal screening and underwriting, Excel credit modeling, borrowing base tracking, covenant testing, back-office reconciliation, and LP reporting. Each engagement scopes to a specific target ROI — we decline projects that do not clear a 3x return on client investment within 12 months.
Why Accretive AI Exists
Private credit fund managers have a specific problem that most AI platforms do not address well. They have the workflow pain — quarterly monitoring cycles consuming 200–300 analyst hours per quarter, borrowing base monitoring absorbing days of monthly work, amendment tracking degrading across portfolios, hardcoded banker models that break on first stress test — but they don't have the internal technology resources to deploy enterprise AI platforms, and generic AI tools like ChatGPT or Claude used without credit-specific configuration produce inconsistent results that analysts rightly distrust.
What funds actually need is working infrastructure that meets them where they already operate: no new systems to adopt, no vendor lock-in, no expensive platform commitments, no learning a new UI. The infrastructure runs on the tools the team already uses — Excel, Claude, Google Workspace, SharePoint — with the credit expertise layer sitting on top. The practitioner encodes the judgment; the analyst consumes the output.
Accretive AI was founded on a specific thesis: the value in vertical AI lives in the input layer — the prompt architectures, skill files, borrower configurations, and encoded domain judgment — not in the model itself. Senior credit practitioners encoding their own work standards into AI workflows produce output quality that generic tools cannot match, and funds can access that expertise as a service without building internal AI capabilities.
For the full articulation of this thesis, see our blog post The Input Layer Thesis for Private Credit.
The Team
Accretive AI is led by a team with 15+ years at leading private credit managers and billions deployed across first-lien direct lending, unitranche, second-lien, asset-based loans, mezzanine, and distressed/special situations investments. The team's sponsor network spans a majority of the tier-1 middle-market private equity ecosystem, with direct coverage relationships maintained over decade-plus careers.
Every AI workflow Accretive AI ships is designed by someone who has built the credit models, written the IC memos, monitored the borrowers, negotiated the amendments, and worked out the distressed deals being encoded into the system. The quality signal in Accretive's work is the depth of the practitioner judgment embedded in the prompts and skills — not the choice of AI platform.
Contact: KD@goaccretive.ai
Service Tiers
Accretive AI engagements fall into three tiers, priced and scoped to match different levels of infrastructure investment. Most clients start at Augment and expand into Automate or Architect as the ROI compounds. Timelines below are typical; actual duration scales with scope, portfolio size, and workflow complexity.
Augment Tier
Typical engagement: $5,000 – $20,000 one-time sprint, scoped to the workflows being configured. Optional ongoing retainer: $2,500 – $5,000 per month. Typical duration 4–6 weeks, scope-dependent.
What's included (scope varies by client and by cash flow vs. ABL focus):
- Configured Claude Projects for defined credit workflows (screening, prescreen memos, portfolio monitoring, borrowing base monitoring, IC memo preparation)
- System prompt architecture — standing instructions encoding the client's credit box, memo format conventions, communication tone, and escalation logic
- House style guide — one-page reference directing AI outputs on what to emulate from precedent work and what to ignore
- Project knowledge base seed — credit policy, precedent memos, IC templates, standard reporting formats
- Credit Model Standards Skill — structured skill file encoding ten rules that AI-generated or AI-updated credit models must follow, from dynamic debt waterfalls to proper FCF construction to covenant testing discipline
- Workflow skills tailored to the client's primary use case — quick deal evaluation, prescreen memo, IC memo, quarterly monitoring memo, variance analysis
- Borrowing Base Tracker Workbook for ABL clients — template Excel structures with borrower-specific advance rate and eligibility logic, cross-aging, concentration, and reserve calculations
- Claude for Excel enablement — sidebar configuration across team seats, Excel Document Q&A skill, usage guide covering session management and model audit patterns
- Daily news monitoring automation — scheduled task that pulls news on the client's portfolio and priority industries, synthesizes credit-relevant items, delivers email brief
- Prompt cheat sheet — 1-page reference card for management call prep, call summary, borrower communication, document Q&A
- Sample runs and validation on real client deal flow or borrowers, refined with client feedback until production-ready
- Kickoff training session (60–90 minutes) plus user guide for each delivered workflow
- 30 days of post-delivery support for refinements
Who this fits: Funds that want to validate AI workflow value before committing to infrastructure. Firms whose analysts are already using Claude or ChatGPT informally and need structure imposed. Teams that want a credible pilot before expanding.
Automate Tier
Typical engagement: $5,000 – $30,000 per project depending on scope, plus $2,500 – $12,500 per month ongoing retainer. Typical implementation duration 8–12 weeks, scope-dependent.
What's included (scope varies by client and by cash flow vs. ABL focus):
- Everything in Augment, plus:
- Full portfolio monitoring system — intelligence layer, output workflows, borrower onboarding in waves, integrated meeting-prep workflow, live dashboard
- Portfolio Monitoring Skill — comprehensive skill file encoding monitoring framework: memory schema, scoring, trigger evaluation, meeting-prep logic, quarterly memo generation
- Borrower memory layer — structured per-borrower context file capturing identity, facility details, covenant package with step-downs, capital structure, 8 quarters of historical financials, operational snapshot, watchlist status, call log, memo log, material events, open diligence items
- Scoring rubric and watchlist triggers — weighted multi-signal framework producing consistent watchlist ratings; codified hard/soft triggers and response patterns; documentation layer with analyst retaining discretion
- Quarterly monitoring memo workflow — generates the client's quarterly memo from reporting package + borrower memory + prior-period memo; updates per-borrower Excel tracker with the latest reporting period data
- Meeting prep workflow — integrated skill producing 1-page brief for any scheduled management call; post-call note capture updates call log and open items
- Firm-level borrowing base reporting (for funds with facility lenders) — monthly automation of the fund's own borrowing base certificate; Excel workbook plus narrative cover letter in the required lender format
- Prescreen memo workflow — VDR ingestion to 8–12 page prescreen memo plus back-of-envelope credit model; QoE analysis skill, model audit skill, multi-stage workflow chain with validation checkpoints
- IC memo and credit model build — paired deliverable: full credit model build automation and full IC memo generation, cross-referenced and validated together
- Agent-driven workflows deployed via Anthropic SDK, running scheduled or on-demand
- Per-borrower configurations — structured configuration files for each borrower capturing credit agreement specifics, covenant schedules, model structure, and eligibility logic
- Portfolio dashboard — live dashboard showing watchlist by borrower, covenant cushion heat map, upcoming reporting dates, material events log, trend indicators
- Back-office automation for specific operational workflows (cash reconciliation, notice tracking, reporting calendars)
- Ongoing monthly maintenance: new borrower onboarding, workflow refinement, skill tuning with written change-log, monthly check-in call
- Quarterly business reviews and training refreshers; quarterly workflow performance audit
Who this fits: Funds that have decided AI workflows are worth embedding into their operating model and want a partner to own the technical layer while they focus on investment work. Teams ready to move beyond one-off prompts into production workflows.
Architect Tier
Typical engagement: $10,000 – $25,000 per infrastructure component, plus $5,000 – $15,000 per month ongoing retainer. Typical per-component duration 6–16 weeks, scope-dependent.
What's included (scope varies by client and workflow focus):
- Everything in Automate, plus:
- Custom MCP connectors — tool integrations that let AI systems read directly from client infrastructure (Allvue, Everest, Salesforce, SharePoint, loan management systems)
- Production pipeline builds — scheduled, event-driven, and on-demand workflows deployed to client infrastructure or to Accretive-hosted infrastructure with client data isolation
- Internal dashboard builds — read-only browser-based dashboards that visualize portfolio monitoring outputs, covenant trajectories, and workflow status
- Human sign-off workflows — structured review and approval interfaces for workflows subject to SEC Rule 204-2 audit-trail requirements
- Full DevOps and infrastructure ownership, including monitoring, logging, and incident response
- Quarterly strategic reviews with the client's operating leadership
Who this fits: Funds ready to build AI-native operational infrastructure as a platform layer, typically at a stage where operational leverage is material to investment capacity. Registered Investment Advisor clients needing audit-trail-complete workflows.
Target Market
Accretive AI works primarily with fund managers matching the following profile:
- AUM band: $200 million to $20 billion (primary). Larger managers engaged on targeted workflow builds and as the bridge between internal IT and investment teams.
- Strategy: Direct lending, unitranche, asset-based lending, specialty finance, opportunistic credit, distressed credit
- Portfolio size: 10 to 200 active borrowers
- Team size: 5 to 50 investment professionals
- Internal technology resources: Typically limited — no dedicated data engineering or AI staff — though we also work with larger managers whose IT teams need a credit SME to design prompts, skills, and memo frameworks
- Existing AI exposure: Usually moderate. Analysts using ChatGPT or Claude informally; no configured workflows; frustration with inconsistency of outputs
What Makes Accretive AI Different
Four things distinguish our delivery model:
1. Practitioner-led input layer
Every prompt, skill, and configuration Accretive ships is authored by someone who has done the work being encoded. This is the single most visible quality signal in outputs: analysts recognize immediately when AI-generated work reads like an analyst who has lived the workflow versus a tool that has been trained to guess.
2. Service, not software
Clients consume finished outputs. They do not learn a new tool, adopt a new platform, or train their team on AI operations. No new systems. No expensive platform investments. No vendor lock-in. No learning a new UI. The infrastructure runs on tools the team already uses — Excel, Claude, existing file storage — with the credit expertise layer sitting on top.
3. Tool-agnostic execution
Claude is our primary platform, but we use NotebookLM, Gemini, ChatGPT, and Perplexity wherever they produce the best result. Clients benefit from the best tool for each workflow without being locked to any single vendor. When a better tool emerges, we swap it in; the workflow outputs remain consistent. See our AI Vendor Landscape for Private Credit for the current state of the market.
4. Compounding configuration
Per-borrower configurations, encoded skills, and borrower memory layers accumulate over the course of an engagement. The longer Accretive operates inside a client, the higher the quality and consistency of outputs — and the higher the switching cost back to manual. This is the input-layer thesis expressed mechanically.
Security, Compliance, and Data Handling
Accretive AI operates under a data handling framework appropriate to regulated financial services clients.
- Zero-data-retention AI usage: Client data processed via the Anthropic API is subject to zero-data-retention terms. Data is not retained by the AI provider and is not used for model training.
- Client data isolation: Each client engagement operates in a logically separated environment. Configurations, memory files, and outputs are segregated.
- Encryption at rest and in transit: AES-256 at rest, TLS 1.2+ in transit.
- Insurance coverage: Technology errors and omissions (E&O) and business owner's policy (BOP).
- MSA and SOW framework: Standard Master Services Agreement and Statement of Work templates available on request.
- Scope of data handled: Borrower financial data, credit models, credit agreements (for covenant extraction only), portfolio reporting data, and fund-level information. We do not process PII, LP personal data, or consumer financial data subject to GLBA or CCPA.
A complete Data Handling and Security Overview is available on request.
Frequently Asked Questions
What is Accretive AI?
Accretive AI is a services firm that builds AI-driven workflows for private credit and ABL fund managers. The firm was founded on the thesis that vertical AI value lives in the input layer — prompt architecture, skill files, and encoded domain judgment — rather than in the underlying AI model. Our team has 15+ years of private credit experience and billions deployed across direct lending, ABL, leveraged finance, and special situations.
How is Accretive AI different from a SaaS platform?
SaaS platforms sell software your team operates. Accretive AI delivers finished workflows — your team consumes outputs, not tools. This fits firms that want practitioner-authored configurations rather than generic templates, and firms that want to avoid the adoption curve of a new platform. It also fits firms that are explicitly concerned about vendor lock-in during a consolidation cycle. See our AI Vendor Landscape for Private Credit for detailed comparisons.
Who founded Accretive AI?
Accretive AI was founded by a team of private credit practitioners with 15+ years across direct lending, ABL, leveraged finance, and special situations. The team has deployed billions of dollars of capital across the capital structure at leading private credit managers.
Does Accretive AI work with ABL shops specifically?
Yes. ABL workflows — borrowing base monitoring, concentration and eligibility testing, cash dominion reconciliation, field exam preparation — are a core vertical for Accretive AI. The Credit Model Standards Skill, borrowing base tracker templates, and per-borrower configuration framework are all designed to accommodate ABL-specific requirements that generic credit tools handle poorly.
Does Accretive AI work with larger managers?
Yes. The primary focus is the $200M–$20B AUM band, but larger managers engage Accretive in two shapes: targeted workflow builds (a specific skill, agent, or configuration) where practitioner-designed input layer adds value; and bridge work between internal IT teams and investment teams — IT owns tenant configuration and deployment plumbing, Accretive owns prompt architecture, knowledge curation, memo templates, and QA frameworks. Deal teams at large managers are time-constrained, and IT teams cannot produce credit-grade prompts and skills without a domain SME. That's where Accretive sits.
What AI platforms does Accretive AI use?
Claude is our primary platform. We also use Gemini, ChatGPT, NotebookLM, and Perplexity wherever appropriate. We are tool-agnostic: the best tool for each specific workflow wins.
How long does a typical engagement take to deliver results?
An Augment-tier engagement typically delivers configured workflows within 4 to 6 weeks. An Automate-tier engagement reaches steady-state quarterly monitoring after approximately 8 to 12 weeks, including borrower onboarding. Architect-tier engagements are scoped per component, typically 6 to 16 weeks per component. All timelines scale with scope.
Does Accretive AI sign MSAs and SOWs?
Yes. Standard Master Services Agreement and Statement of Work templates are available on request. Data Handling and Security Overview document is also available and can be shared for vendor KYC and procurement processes.
What happens to my configurations if Accretive AI were ever acquired or wound down?
Client configurations, skill files, prompt architectures, and borrower memory layers remain the property of the client in every engagement. Our MSA includes provisions for configuration export and transition support. Clients receive the underlying files — we do not hold the input layer hostage.
Contact
Email: KD@goaccretive.ai Web: goaccretive.ai
For engagement inquiries, vendor KYC, or partnership conversations, reach out directly. We respond to all practitioner inquiries within one business day.