Step 01
Upload documents
Bring in PDFs, contracts, filings, briefs, and other text-heavy materials. LATCH treats the corpus as a unified set rather than isolated chunks.
LATCH v2.0 | Persistent Document Intelligence
Compile your documents once. Query across them in milliseconds.
LATCH transforms full document sets into persistent compiled representations. Cross-document queries that usually take seconds can return in under 200ms, with stronger answer quality than standard chunk-and-retrieve workflows. Built for due diligence, M&A, and enterprise teams working across many files.
40×
Faster Time-to-First-Token
+36%
Cross-Doc Answer Quality
28×
Amortization at Scale
97%
Cost Reduction vs. Baseline
The Problem
Every query reprocesses every document. Teams pay the cost every time.
Standard AI pipelines read the full document set from scratch on every question. For a 200-document data room queried by a 10-person deal team, that becomes thousands of redundant full-context passes: slow, expensive, and identical to the last run.
01
Seconds per query, not milliseconds
Standard LLMs can take 4 to 6 seconds to produce a first token on multi-document queries. Analysts wait, momentum breaks, and teams lose hours to latency.
02
Cost scales with every question
Each query reprocesses the same corpus. Ten analysts asking twenty questions each creates 200 full-context passes over the same documents.
03
Cross-document reasoning is fragile
Chunk-and-retrieve systems lose context at document boundaries. Answers that depend on connecting clauses, disclosures, and schedules across files often degrade.
How LATCH Works
Three steps. One compilation. Unlimited queries.
LATCH transforms a document set into a persistent compiled form. The heavy lift happens once. After that, every additional question benefits from the same compiled representation.
Step 02
Compile once
A proprietary encoding process transforms the corpus into a compact persistent representation. The compilation is measured in seconds, not minutes.
Step 03
Query instantly
Cross-document reasoning runs against the compiled representation with sub-200ms latency. The more queries a team asks, the greater the performance advantage.
See It In Action
From file selection to cross-document inference in seconds.
Watch LATCH compile a document set and answer cross-document queries live. No edits, no acceleration — real-time captures of the system running on GPU hardware.
File Selection and Compilation
Selecting documents and compiling them into a persistent LATCH representation
Multiple Document Inference
Cross-document queries returning in milliseconds against the compiled corpus
Measured Results
Benchmarked on real legal and financial documents.
Measurements shown on H100 hardware against a standard baseline model processing the same corpus. Test materials include SEC filings, credit agreements, antitrust briefs, commercial leases, and regulatory frameworks.
Speed & Economics
| Metric | Baseline | LATCH |
|---|---|---|
| Time to first token | 4.47 s | 0.11 s |
| End-to-end response | 6.55 s | 2.02 s |
| Average TTFT speedup | 1× | 42.9× |
| Average E2E speedup | 1× | 5.2× |
| 25-query amortization | 1× | 28.5× |
| Cost per session | $0.176 | $0.004 |
Cross-Document Quality
| Task | Baseline | LATCH |
|---|---|---|
| Cross-doc retrieval | 0.394 | 0.534 |
| Cross-comparison | — | 0.594 |
| Cross-format reasoning | — | 0.662 |
| Selective retrieval | — | 0.677 |
| Multi-doc gate accuracy | — | 11 / 12 |
| Single-doc accuracy | — | 11 / 12 |
Quality measured as weighted token-F1 on customer-shaped document packs. Full methodology is available on request.
Model Portability
Validated across four production model families.
LATCH is not tied to a single model. Training, compilation, and benchmarking have been validated end-to-end across multiple model families, with additional ports in progress.
Qwen 2.5
14B parameters
TTFT speedup42.9×
Cross-doc F10.534
Multi-doc gate11/12
StatusPrimary
Mistral Nemo
12B parameters
TTFT speedup104×
Cross-doc F10.659
Multi-doc gate10/12
StatusComplete
Llama 3.1
8B parameters
TTFT speedup116×
Cross-doc F10.610
Multi-doc gate10/12
StatusComplete
DeepSeek-R1
14B parameters
TTFT speedup43×
Cross-doc F10.403
Multi-doc gate9/12
StatusComplete
Built For
Anywhere teams ask many questions across many documents.
The more frequently a team queries the same corpus, the more attractive the economics and latency profile become.
M&A Due Diligence
Compile a data room once, then let every analyst ask sub-second cross-document questions across financials, contracts, and disclosure schedules.
Commercial Due Diligence
Evaluate acquisitions across hundreds of files under time pressure without paying to reprocess the same corpus on every question.
Contract Intelligence
Compare clause language across agreements, amendments, and side letters instantly, and surface conflicts across related transaction documents.
Regulatory & Compliance Review
Cross-check filings, policies, and governance materials in environments where speed, privacy, and auditability matter.
About
I hold 13 patents across multiple technology domains and have spent 20+ years turning computational bottlenecks into competitive advantages.
LATCH was designed, built, trained, and benchmarked end-to-end by me, from architecture design through multi-model porting, benchmark infrastructure, and cloud-based training operations on H100 hardware.
CoDynamics Lab Corporation is a Delaware C-Corp based in Gilbert, Arizona, focused on persistent document intelligence for enterprise workflows where speed, accuracy, and cost define competitive advantage.
See it on your documents.
Live demo available. Bring your own corpus, we compile it in seconds, and you query it live across the full document set.