Investor brief · Confidential · 2026
Every knowledge-intensive profession runs the same pipeline: gather documents, extract data, apply logic, make a decision. Today that pipeline is entirely manual, and it does not get smarter over time. LOOP fixes both.
LOOP automates the pipeline from documents to decisions — and gets smarter every time an expert uses it.
For context: DocuSign automates one stage of this pipeline and is worth $9B. LOOP automates all four — across 15 verticals.
The pipeline
Read any document. Populate any logic model. Produce a verified, cited decision output. The model does not have to be ours — it only has to exist.
LOOP connects any document to any decision model — and turns every correction into a competitive advantage.
The core insight
Every correction an expert makes is logged automatically. The system gets smarter through normal daily use — no training programs, no labeling contracts. The correction log is the moat: proprietary by nature, compounding by design.
The mechanism below is illustrated through private lending — one of many applicable contexts. The same loop runs identically for an insurance adjuster reviewing a claim, an attorney working through due diligence, a credit officer underwriting an SBA loan, or any professional whose work moves from documents to decisions.
Extracted values surfaced with source citations. Accept, correct, or flag each field — inline, in seconds.
Accept, correct, flag, dismiss — each recorded with source document, value delta, timestamp.
Prompt logic updated immediately. Fine-tuning dataset accumulates for future model training.
Fewer corrections per decision. Switching cost grows with every decision processed.
“The correction log cannot be purchased or replicated. It exists only inside organizations that use LOOP — and grows more valuable with every decision processed.”
Vertical strategy
Same pipeline across every regulated profession. The mismatch between decision volume and ease of adoption is the strategy: start with small markets reachable in weeks, earn the right to large markets that take years.
Plus six more verticals identified — Factoring & Invoice, Corporate Functions, Government & Regulatory, Commercial Banking Credit, Venture Debt, Family Office. See all 15 by volume or by ease of adoption.
Ranked by total addressable decision volume — largest to smallest. Color indicates priority tier.
| # | Vertical | Why it qualifies | TAM |
|---|---|---|---|
| 1 | Healthcare Prior Auth Year 3 | Billions of decisions/yr across all US payers. | $340B+ |
| 2 | Commercial Banking Credit Future | Every US bank underwrites business loans. Massive scale. | $200B+ |
| 3 | Corporate Functions Future | Procurement, AP, HR screening, ESG globally. | $180B+ |
| 4 | Insurance Underwriting Year 2 | Commercial property, specialty lines, claims. | $120B+ |
| 5 | Legal Due Diligence Year 2 | $350B US legal market — document review is majority of associate hours. | $80B+ |
| 6 | Government & Regulatory Future | Permit review, grant administration, examination. | $60B+ |
| 7 | Commercial Real Estate Year 2 | $20T asset class, every deal document-heavy. | $40B+ |
| 8 | PE & Asset Management Year 3 | Deal screening, portfolio monitoring. High value per decision. | $30B+ |
| 9 | SBA & Community Banking Near-term | Larger institutional footprint. More standardized but still manual. | $20B+ |
| 10 | Equipment Finance Near-term | Large asset base, clear collateral documentation. | $15B+ |
| 11 | Construction Near-term | Draw review, contractor prequalification, change orders. | $12B+ |
| 12 | Private Lending Live | Highest pain per operator. Live deployment underway. | $8B+ |
| 13 | Factoring & Invoice Year 2 | High volume, simpler document types. | $6B+ |
| 14 | Venture Debt Future | Niche. Relationship-driven, limited scale. | $2B+ |
| 15 | Family Office Future | Very few operators. High value per decision, minimal scale. | $1B+ |
Ranked by how quickly each vertical moves from first conversation to active paying customer.
| # | Vertical | Why this order | Time to first $ |
|---|---|---|---|
| 1 | Private Lending | Live. Colonial deployment underway. Short sales cycle, acute pain, documents in SharePoint. | Weeks |
| 2 | Construction Lending | Adjacent to private lending. Same buyer profile, same doc infrastructure. | 1–2 mo |
| 3 | SBA & Community Banking | Underserved by software. Document types overlap heavily with live build. | 2–3 mo |
| 4 | Equipment Finance | Small shops, clear document set, similar buyer to private lending. | 2–4 mo |
| 5 | CRE Brokerage | CoStar/Crexi integrations already identified as the unlock. | 3–4 mo |
| 6 | Factoring & Invoice | Simpler documents, high volume, clear ROI. | 3–5 mo |
| 7 | PE & Asset Management | Sophisticated buyers. Longer sales cycles, more customization. | 4–6 mo |
| 8 | Commercial Insurance | Real pain, real budget. Buyer inside a larger institution. | 6–9 mo |
| 9 | Legal Due Diligence | High value. Conservative adopters — partner-level sale required. | 6–12 mo |
| 10 | Corporate Functions | Large enterprise buyers. Multi-stakeholder, long cycles. | 9–12 mo |
| 11 | Healthcare Prior Auth | Largest by volume. HIPAA, payer-specific rules, entrenched incumbents. | 12–18 mo |
| 12 | Family Office | Purely relationship-driven. Warm intro or nothing. | Unknown |
| 13 | Venture Debt | Too few operators. Too relationship-driven to scale. | Unknown |
| 14 | Government | Enormous volume. Procurement cycles measured in years. | 2–4 yr |
| 15 | Commercial Banking | Largest buyers. Longest cycles. Most entrenched systems. | 2–5 yr |
Roadmap
A second document intake layer is planned for external data sources — property records, county assessors, CoStar, Crexi, comparable sales platforms. Private lenders pull this data manually for every deal. Automating external fetch addresses their three largest stated pain points: doc-chasing, data entry, and data spreading.
The sweet spot
Serviceable obtainable market in private lending, construction lending, and SBA / community banking. Same buyer profile. Same document infrastructure. Same acute pain. Reachable inside three years — before a single enterprise sale is required.
Market opportunity · Two frames
Knowledge worker time on document-to-decision workflows globally. ~25M target professionals at $80–200K loaded cost. Recapture 20% → $1T+ economic value → SaaS captures a fraction.
15 identified verticals × operators × realistic seat count × price point. IDP market projected $49.7B by 2035 at 32% CAGR (Spherical Insights, 2025).
US-based regulated professional services across LOOP's five near-term verticals. ~20,000 target firms × $500–$2,000/mo per firm.
Private lending, construction lending, and SBA in the US. ~25,000 target lenders × 10% penetration × $200/seat/mo × 2 seats avg, over 3 years.
Competitive landscape
LOOP is not priced as a replacement for any single tool below. It replaces the combination — plus the hours of manual work that sit between them.
| Tool | What it does | Price | Gap LOOP closes |
|---|---|---|---|
| Ocrolus | Document processing / bank stmt analysis | $500–2,000/mo | Extraction only — no logic, no verification UI, no flywheel |
| Blooma | CRE deal analysis platform | $1,000–3,000/mo | Single vertical, no model import, no doc-to-cell mapping |
| nCino | Bank operating system / loan origination | $50,000+/yr | Enterprise sales, 12+ month implementation |
| Docusign | E-signature and document workflow | $25–40/user/mo | Signature only — no extraction, logic, or decision output |
| ABBYY Vantage | Intelligent document processing (IDP) | $3,000–15,000+/mo | Horizontal IDP — no domain flywheel, no model import |
| Microsoft Copilot | AI assistant embedded in Office | $30/user/mo | Horizontal — no vertical depth, no underwriting logic |
| LOOP | Docs → Data → Logic → Decisions pipeline | $299–1,799/mo | Full pipeline — extraction, model import, verification UI, correction flywheel |
Pricing
A mid-level underwriter runs $48/hr fully loaded. LOOP recaptures 3.3 hours per decision. At ten decisions a month that is $1,584 of monthly labor value recaptured. The Starter tier costs $299. Every tier delivers a minimum 4× ROI.
Run the math
Move the sliders to model your shop. The math mirrors the Colonial deployment: 4.0 hrs → 0.7 hrs per decision, 3.3 hrs recaptured.
Revenue scenarios
All three share the same tiers and addressable market. What differs is adoption speed, churn rate, and tier mix. The single variable with the largest impact is monthly churn — at 1% the correction flywheel has done its job; customers cannot leave without abandoning institutional knowledge encoded over dozens of decisions.
All ARR figures computed live from new customers, tier mix, churn rate, and expansion. Adjust the toggle above to see how the simulation responds.
Traction
Live deployment with a private lending operation in Phoenix, AZ — 30 active deals in pipeline, design partner paying and engaged.
Correction logging instrumented — every accept, correct, flag, dismiss captured with source document, value delta, and timestamp. The flywheel is running.
SharePoint integration complete — real deal folders ingested and classified, Sites.Read.All permission scope authenticated.
Excel underwriting model import — existing lender models connected without rebuilding, formula preservation, cell-to-doc mapping.
Extraction tested against 30 real deals — retail, multifamily, mixed-use, non-standardized borrower documents across all types.
Full-stack application live at colonial-os.com — React + FastAPI + Postgres on Hetzner, white-labeled for the design partner.
Why now
Frontier AI models crossed the extraction quality threshold in 2024 — reliable enough to be useful on day one, correctable enough to compound continuously. The window to build a proprietary correction dataset before a well-funded incumbent enters this space is open. It will not stay open indefinitely.
Frontier models can now extract structured data from non-standardized documents with enough accuracy to be useful immediately. Two years ago this wasn't true.
nCino serves banks. Ocrolus serves mortgage. No one is purpose-building for the private lending workflow with a compounding data moat. The category is uncontested.
Most founders spend six months finding a design partner. LOOP started inside one. The correction dataset is already accumulating. The flywheel has already started.
Funding strategy
Plan: bootstrap through Colonial and the first five external customers. One paying customer proves the product works, not that strangers will buy it. Every month spent building the correction dataset deepens the flywheel and strengthens the negotiating position for any future raise. The trigger for the first round is a single chart: extraction accuracy on decision one versus decision fifty.
The chart that unlocks everything
Extraction accuracy on decision one versus extraction accuracy on decision fifty. If that number moves materially, the correction flywheel is empirically real. That single chart — not the memo, not the financial model — converts a curious investor into a convinced one. Building the instrumentation to capture it starts on day one.
Next steps
For prospective customers
If your team underwrites commercial real estate, construction, or SBA loans and spends significant time on document gathering and data entry, LOOP was built for you. Design partners receive preferred pricing, direct influence over the roadmap, and early access to the process library as it develops.
For investors
The decision to raise will follow proof of the feedback flywheel in a live multi-lender environment. Conversations with aligned investors are welcome now — long before any term sheet. The pre-seed trigger is a single chart: extraction accuracy on decision one versus decision fifty.
LOOP is the infrastructure layer that converts document-heavy workflows into automated decision pipelines that compound over time.