Orange Collective
Unsiloed AI

Unsiloed AI

API for parsing multimodal unstructured data into LLM-ready JSON and Markdown.

unsiloed.aiYYC Profile@UnsiloedHQLinkedIn
Unsiloed AI — Vision-First Document Extraction[1]

$24k

MRR

Profitable at this scale · 14 paying customers

1M+

Pages / week

Across Fortune 150 banks + NASDAQ-listed cos.

$500k

Signed LOI

Global bank · 15 ongoing pilots

Thesis

Unsiloed AI is an API that converts complex multimodal documents (PDFs, scans, slides, charts) into structured, LLM-ready data with a vision-first pipeline — not an LLM-first one — built for speed, determinism, and enterprise deployment (incl. air-gapped on-prem).[1] The market is shaping up like AI code (Codex / Cursor / Replit) and AI legal (Harvey / Eve / Crosby) — an industry, not a winner-take-all market. Unsiloed is the horizontal ingestion layer for regulated, multimodal workflows.[19]
  1. 01

    Deterministic, auditable parsing is mandatory for regulated AI. Banks, insurers, legal, and healthcare teams need reproducible, explainable outputs with layout preservation and confidence scoring. LLM-only pipelines are probabilistic, drift with model updates, and are hard to audit. Unsiloed produces deterministic, schema-aligned, citation-backed outputs.[1]

  2. 02

    Vision-first beats LLM-first on speed and cost. Vision models parallelize on GPUs; 7B-scale LLMs in the extraction loop drive higher latency, nondeterminism, and per-page token cost. Unsiloed's ~$0.01/page economics undercut Reducto's ~2× per-page pricing.[15]

  3. 03

    A proprietary corpus becomes a compounding moat. 1M+ real multimodal documents and domain ontologies for finance, legal, and healthcare. Customer-specific post-training on low-confidence fields and corrections feeds back into the model — a reinforcement loop that compounds over time.

  4. 04

    The team is the canonical technical pair for this stack. Aman (ultra low-latency trading in C++/Rust; AI copilots for Goldman / Schwab) and Adnan (MIT; multimodal models at a Fortune 10; autonomous perception at Mercedes). Both IIT Kharagpur. Already shipping into Fortune 150 banks and NASDAQ-listed enterprises.[1]

Problem

AI teams spend 6+ months building document workflows. Fewer than 10% reach production.

Generic LLM parsers and OCR collapse on multimodal documents that contain text, tables, images, and charts. Poor parsing and suboptimal chunking cripple RAG pipelines and downstream automation. The proof-of-concept demo passes; the production rollout doesn't.[1]

Financial, insurance, legal, and healthcare documents are not text-only. They frequently contain charts, infographics, styled text, footnotes, merged cells, multi-page tables, color-encoded semantics, and irregular multi-column layouts. These structures carry meaning that generic LLM parsers routinely miss, conflate, or hallucinate.

More importantly, LLMs cannot parse these multimodal elements deterministically — making them unsuitable for high-stakes, auditable extraction. A bank's reconciliation pipeline can't tolerate non-determinism; an insurance claim adjudicator can't accept "the parser sometimes flips merged cells."

80%

Of enterprise data

Is unstructured · only a fraction is analyzed

<10%

Of doc workflows

Reach production after 6+ months of build

1M+

Documents in corpus

Proprietary, multimodal, domain-tuned

Why Now

AI document extraction is an industry, not a market.

Like AI code (Codex / Cursor / Replit) and AI legal (Harvey / Eve / Crosby), document extraction will support multiple horizontal infra players and vertical specialists. Unsiloed is positioned as the horizontal ingestion layer for regulated, multimodal workflows.

Industries, not markets. AI categories like code, legal, and document extraction are not winner-take-all — they support multiple horizontal infrastructure players and vertical specialists.

Anish Acharya

Anish Acharya[19]

General Partner · Andreessen Horowitz

The unstructured-to-AI layer is becoming core infra.

The data problem is enormous. 80% of enterprise data is unstructured; only a fraction is analyzed. Turning documents into AI-ready data is becoming as fundamental as databases were in the last era.[2]

IDP is growing at 30%+ CAGR. Intelligent Document Processing is projected to reach multi-tens of billions by the early 2030s. Banking and financial services lead adoption, followed by healthcare and legal.[3]

Document AI: ~$12–13B → ~$27B by 2030. Spend is rising as enterprises move from pilots to production RAG and agents. The teams that bet on LLM-only extraction in 2024 are now realizing they need the deterministic infra layer underneath.[4]

Industries, not markets. AI document extraction will support multiple horizontal infrastructure players and vertical specialists — just like AI code and AI legal.
— Anish Acharya, General Partner · a16z[19]

Product & Technology

Segment visually → preserve structure → decode deterministically.

The full pipeline is engineered around the principle that LLMs should not be in the extraction loop for regulated workflows.

Layer 01

Region segmentation & layout

Specialized vision models detect text blocks, multi-page tables, figures, and charts. Heatmap-based chunking keeps semantically related content together across page breaks — preserving meaning the way a human reader would.

Layer 02

Dual-stream representation

Parallel streams preserve (a) textual content and (b) layout / format (hierarchy, indentation, alignment). Captures cues like right-aligned subtotals and merged cells that are critical to finance and legal extraction.

Layer 03

Domain-tuned decoders with RL

Output normalized JSON and Markdown with citations and per-field confidence scores. Domain-specific decoders trained with RL pipelines. Designed for downstream RAG and agent pipelines that need verifiable, schema-aligned output.

Layer 04

Deployment & security

Cloud API or fully air-gapped on-prem. SOC2-aligned posture. No human-in-the-loop on the vendor side. Confidence-gated human review on the customer side — exactly the audit trail regulated buyers require.

Multimodal strengths where generic OCR + LLM parsers fail.

Charts and infographics. Unsiloed reads tables, charts, and infographics directly — extracting values from axes, legends, and series. Generic OCR collapses to raw text; generic LLM parsers hallucinate the numbers. Unsiloed treats the chart as the structured object it actually is.

Long-tail layouts. A proprietary corpus of 1M+ real multimodal documents and domain ontologies for finance, legal, and healthcare enables higher fidelity on long-tail structures — nested tables, multi-page figures, format-encoded semantics — that generic models consistently miss.

Synthetic post-training. The team also post-trains on synthetically generated multimodal datasets that mimic rare layouts, edge cases, and domain-specific templates — expanding coverage where real-world labeled data is sparse.

Forward compatibility. The architecture is model-agnostic. It can incorporate emerging OCR-free vision-RAG (ColPali) and VLM components as they mature — without abandoning the deterministic decoding and confidence scaffolding that probabilistic LLM-only stacks fundamentally lack.[14]

On public benchmarks, Unsiloed AI consistently outperforms solutions from LlamaIndex, Gemini, Mistral, and Unstructured.io.
— Unsiloed AI launch post[1]

Traction

Already shipping into the buyers most others can't access.

$24k

MRR (~$300k ARR)

Profitable at this scale

14

Paying customers

Fortune 150 bank · NASDAQ-listed cos. · 10+ YC startups

100%

Daily API use

Every paying customer uses the API every day

Pipeline depth that unlocks 6- to 7-figure ACVs.

Volume. Millions of pages processed weekly. The API is in the hot path for production reconciliation, document review, and RAG ingestion at Fortune 150 banks and NASDAQ-listed enterprises.[1]

Pipeline. 120+ companies in pipeline. 15 ongoing pilots — including Rippling and a large public tech company. Single bottoms-up logos in finance and legal land at tens to hundreds of thousands per account; Fortune 500 deployments scale into 7-figure ACVs across business units.

Signed enterprise LOI. $500k LOI with a global bank. This is the early enterprise signal that the deterministic, auditable, air-gapped product positioning lands with the buyers Reducto and LlamaParse are chasing.[1]

Market

Unstructured-to-AI is the next core infrastructure layer.

80% of enterprise data is unstructured; only a fraction is analyzed. Turning documents into AI-ready data is becoming as fundamental as databases were in the last era.[2] Every production RAG system, every vertical AI agent, every regulated automation pipeline needs the ingestion layer underneath.

IDP is growing at 30%+ CAGR to multi-tens of billions by the early 2030s. Banking and financial services lead adoption, followed by healthcare and legal.[3] Document AI alone is roughly $12–13B in 2024 → ~$27B by 2030 as enterprises move from pilots to production.[4]

Bottoms-up wedge — AI startups + data-heavy mid-market

Finance and legal teams adopt first, at tens to hundreds of thousands per account. YC startup customers integrate the API in days. The bottoms-up motion compounds into reference accounts that warm the top-down enterprise sales motion.

Top-down — Fortune 500 logos at 7-figure ACVs

When scaled across business units, single Fortune 500 logos reach 7-figure ACVs. The $500k LOI is the early signal.[1] Determinism, on-prem deployment, and confidence scoring are exactly the requirements that get an Unsiloed contract through procurement.

Competitive landscape

Three categories of competition. Unsiloed wins on determinism, throughput, and air-gapped deployment.

The market splits into LLM-centric specialists, OSS / DIY toolkits, and hyperscaler APIs. Unsiloed's vision-first architecture is the answer to all three.

Reducto

$108M · Series B

Enterprise document intelligence with hybrid CV+VLM. Strong brand, advanced workflow tools. LLM-heavy extraction increases cost and latency; premium pricing signals — Unsiloed's ~$0.01/page economics undercut by ~2×.[6]

Extend

$17M · Seed+A

Full-stack document processing cloud — sandbox UI, eval / annotation, fine-tuning workflows. 95%+ accuracy claims. Earlier-stage; breadth over deep financial verticalization; on-prem maturity TBD.[9]

Unstructured.io

$40M · Series B

GenAI data layer; broad connectors and format coverage. OSS adoption, gov / defense inroads. General-purpose accuracy on complex layouts lower than specialized stacks like Unsiloed.[5]

LlamaParse (LlamaIndex)

~$27M · LlamaCloud

Parser integrated with LlamaIndex RAG / agents; great DX, low cost, tight RAG integration. Generalist accuracy on edge cases lags specialized vendors — exactly the regulated finance / legal documents Unsiloed targets.[8]

IBM Docling (OSS)

Linux Foundation

Open toolkit for local, layout-aware extraction. Free, runs locally, fast iteration, strong community. DIY integration and maintenance burden; limited domain-specific tuning and support.[13]

Hyperscalers — Google Document AI / AWS Textract / Azure Form Recognizer

Cloud APIs

Deep platform integration and global reach. General-purpose; struggle on complex multimodal finance / legal documents. Not specialized for regulated edge cases — and unable to ship air-gapped on-prem the way Unsiloed does.[7]

Our APIs are already parsing hundreds of thousands of documents for startups and NASDAQ-listed enterprises, powering vertical AI solutions across industries.
— Unsiloed AI launch post[1]

Strategic advantages

Moat
  • Deterministic + confidence-scored. Matches the audit and governance posture regulated buyers actually require.
  • Vision-first cost / throughput. ~$0.01/page vs. LLM-centric incumbents — unit economics compound with volume.[15]
  • Proprietary corpus + domain decoders. 1M+ real multimodal documents and finance / legal / healthcare ontologies create a data moat that compounds with every customer.
  • Air-gapped on-prem. The deployment posture that unlocks BFSI procurement — one that Reducto and LlamaParse don't lead with.

Founder deep dive

The canonical technical pair for vision-first document AI.

The shared foundation. Both Aman and Adnan are IIT Kharagpur alumni — one of the densest concentrations of systems and ML engineering talent in the world. They bring complementary skill sets to a problem that requires both extreme-performance systems engineering and applied multimodal ML research.

Aman — low-latency systems + AI copilots in regulated finance. Started building software systems for high-frequency trading at Teesta Investment after IIT Kharagpur — multi-threaded C++ and Rust optimized for ultra-low-latency execution moving billions on crypto exchanges. Then went founding engineer (#1) at a stealth SF startup building AI copilots for institutions like Goldman Sachs and Charles Schwab — exactly the regulated-finance design constraints Unsiloed now serves. He has lived inside the requirements: deterministic, auditable, integrated with legacy compliance flows.

Adnan — multimodal ML at Fortune 10 scale + autonomous perception. IIT Kharagpur → MIT Masters. Built multi-modal models deployed at a Fortune 10 company. Then was building autonomous navigation systems at Mercedes-Benz R&D — perception pipelines that must hold up under real-time, safety-critical constraints. This is the exact skill stack Unsiloed needs: vision-first models that are layout-aware, domain-tuned, and deployable in regulated environments.

The thesis they bring. Unsiloed's published positioning emphasizes that LLMs cannot deterministically parse multimodal documents — and that the right answer is specialized vision models combined with OCR-based models, dual-stream representation (data + layout), and domain-specific decoders trained with RL. This is not a wrapper company. It is a vision-model and infrastructure company, with founders whose careers were already pointed at this problem.[1]

Why now — and why them. Aman is publicly writing about vision models for enterprise documents (Forbes Business Council). Adnan is building the technical strategy for accuracy-sensitive deployments in finance, legal, and healthcare — including on-prem and air-gapped options that match the realities of BFSI procurement. Together they are the canonical pair for this exact category at this exact moment.

Founders

Aman Mishra

Aman Mishra

Co-founder & CEO

IIT Kharagpur (B.Tech, Industrial & Systems Engineering, CS minor). Previously built ultra low-latency C++/Rust trading systems moving billions at a hedge fund. Founding Engineer (#1) at an SF-based stealth AI copilot startup serving Goldman Sachs and Charles Schwab. Launched a P2P rental platform from his dorm room, scaling it to thousands of orders within 2 months. Forbes Business Council contributor on vision models for enterprise documents.

Adnan Abbas

Adnan Abbas

Co-founder & CTO

IIT Kharagpur (B.Tech) → MIT (Masters). Built multi-modal models deployed at a Fortune 10 company. Was building autonomous navigation systems at Mercedes-Benz R&D. Launched India's first Web 3.0 audio app while in college, scaling it to thousands of users within a month. Leads technical strategy for vision-first, layout-aware multimodal models — including on-prem / air-gapped enterprise options.

Risks & mitigations

Risk

Reducto's scale and GTM outspend in enterprise — $108M raised through Series B with strong brand and advanced workflow tools.

Mitigation

Win bake-offs in finance and legal via deterministic accuracy, chart and table fidelity, and on-prem deployment. Leverage cost and throughput edge for high-volume deals — Unsiloed's ~$0.01/page economics undercut Reducto's ~2× per-page pricing.

Risk

Open-source commoditization from Unstructured.io and IBM Docling — DIY teams may settle for 'good enough' free tools.

Mitigation

Offer SLA'd, air-gapped enterprise deployments and maintain an accuracy lead on long-tail documents with proprietary data and domain ontologies. Reduce integration effort vs. DIY — Unsiloed ships hours-not-months to production.

Risk

Foundation model and VLM improvements (OCR-free vision-RAG, ColPali, etc.) narrow the gap between generic LLM parsing and specialized vision pipelines.

Mitigation

Keep the architecture model-agnostic. Integrate emerging OCR-free vision-RAG and VLM components as they mature, while preserving deterministic decoding, schema enforcement, and per-field confidence scaffolding that probabilistic LLM-only stacks cannot match.

Risk

Enterprise procurement friction — security reviews, compliance certifications, support SLAs, and global support coverage take months at Fortune 500 buyers.

Mitigation

Expand certifications (SOC2, ISO 27001, HIPAA), build 24/7 support tiers and field engineering, and showcase on-prem success and references in BFSI and legal. Use 15 active pilots + $500k LOI to seed reference accounts inside reluctant procurement orgs.

What we're watching

  • Conversion of the $500k LOI with a global bank into a production contract — and the speed at which it expands across business units.
  • Whether the 15 ongoing pilots (including Rippling and a large public tech co.) convert at 6- to 7-figure ACVs.
  • Vertical expansion beyond finance — early signal of healthcare or legal logos at parity accuracy.
  • Reducto's response: does it cut pricing, deepen on-prem, or push deeper into a specific vertical?

References

  1. [1]YC Launch — Unsiloed AI: Make Unstructured Data LLM-Ready
  2. [2]Data Dynamics — Unstructured Data: The Blind Spot CISOs and CIOs Must Solve
  3. [3]Fortune Business Insights — Intelligent Document Processing Market
  4. [4]MarketsandMarkets — Document AI Market
  5. [5]SiliconANGLE — Unstructured raises $40M to make raw data LLM-ready
  6. [6]PR Newswire — Reducto raises $75M Series B
  7. [7]Reducto — Compare: Reducto vs Google Document AI
  8. [8]Reducto — Compare: Reducto vs LlamaParse
  9. [9]Extend — Raises $17M to build the document processing cloud
  10. [10]Y Combinator — Extend company profile
  11. [11]LlamaIndex — LlamaCloud / LlamaParse
  12. [12]AIM Media House — LlamaIndex is building AI agents that understand your data
  13. [13]IBM — Docling's rise: the IBM toolkit turning unstructured documents into LLM-ready data
  14. [14]Microsoft Azure — Introduction to OCR-free Vision RAG using ColPali
  15. [15]Mindee — LLM vs. OCR API: Cost comparison for document processing in 2025
  16. [16]Google Cloud — Document AI overview
  17. [17]AWS — Textract product documentation
  18. [18]Microsoft — Azure AI Document Intelligence (Form Recognizer) overview
  19. [19]Anish Acharya (a16z) — Industries, Not Markets (X)