Lettria
Graph Context Layer that turns enterprise documents and SAP exports into ontology-powered knowledge graphs for traceable AI.
If every AI answer has to survive an audit, Lettria's graph reasoning is worth the ontology work. The published benchmark gap over vector agents — 90%+ multi-hop, 100% numerical, 81.7% overall versus 57.5% — is the kind of claim vector RAG vendors can't match on paper, and the production case studies (Alfa Laval, AP-HP, Leroy Merlin) back it with named numbers. Perseus is free to start with a 30 KB input cap, so a developer can test ontology building before committing. Budget for the Pilot Program if you're serious: 8–12 weeks scoped, with embedded ontologists. Compare against vector-first stacks like LangChain plus a vector database if your use case is simple retrieval; compare against
Verified 3d ago · liveness 63/100 · cite: rightaichoice.com/tools/lettria
- Finance teams structuring regulatory, financial and ESG disclosures
- Healthcare organizations turning biomedical literature into auditable graphs
- Legal teams navigating contracts and evolving regulation
- Engineering groups unifying manuals, specs and industrial documentation
- Real-time conversational use cases that need minimal latency
- Teams wanting an out-of-the-box chatbot with no custom setup
- Organizations that cannot dedicate domain experts to ontology modeling
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Skip Lettria if your use case is latency-sensitive conversational AI or simple retrieval over a handful of PDFs — the ontology work only pays off when your domain is complex, high-value, and has to be explained to an auditor.
The GraphRAG side is scoped per deployment, so the Pilot Program and Production Deployment are quoted by the team rather than listed — budget for ontology planning before you see a number.
Lettria splits into two commercial paths. Perseus, the developer platform, is free to start (30 KB max input, 5 graph builds and 1 ontology build per 30 days) with Plus at $0.01 per PCU for small teams. GraphRAG is scoped enterprise pricing — Pilot Program, Production Deployment, and In-house Experts, all quoted. That puts it above self-serve vector RAG tools and in the same conversation as Palantir or a bespoke consulting engagement.
In short
Lettria — Graph Context Layer that turns enterprise documents and SAP exports into ontology-powered knowledge graphs for traceable AI. Best for Finance teams structuring regulatory, financial and ESG disclosures, Healthcare organizations turning biomedical literature into auditable graphs, Legal teams navigating contracts and evolving regulation. Free to start; paid plans from $0.01.
What people actually say about Lettria — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
19 mentions across 3 sources (Hacker News, YouTube, Product Hunt) · researched Aug 28, 2026.
Average across the 3 sources that answered — each source counts once, not each post.
- +Graph retrieval with full provenance ensures every AI answer is traceable and auditable.
- +Automated ontology building from domain data reduces manual modeling effort.
- +Text-to-Graph pipeline handles documents, tables, and SAP exports efficiently.
- +Multi-hop, numerical, and temporal reasoning capabilities outperform vector-based RAG.
- +Product Hunt community praises accessibility and no-code NLP potential.
- −Public pricing is unavailable, limiting budget planning for potential buyers.
- −Community feedback is sparse; few in-depth user reviews exist online.
- −Enterprise focus may alienate SMBs and individual practitioners.
- −Ontology design and graph modeling require specialized skills.
- −Documentation is mostly YouTube and blog; no API docs for non-customers.
- • Custom ontology engineering may require professional services fees.
- • No clear volume-based pricing for API usage; could scale with data size.
- • Potential infrastructure costs for hosting and maintaining graph database.
Viability Score
How well maintained and how widely used is Lettria? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Automated ontology building from domain data
- Text-to-Graph pipeline for documents, tables and SAP exports
- Graph retrieval with full provenance on every answer
- Multi-hop reasoning across fragmented sources
- Numerical reasoning over structured business data
- Temporal reasoning across changing knowledge bases
- Ontology governance and version control
- Knowledge Studio: graph-native document intelligence platform
- Perseus: infrastructure for production-grade graph agents
- Ontology editor and evaluation tooling
- Native tables, layouts and multimodal content handling
- Forward Deployed team with embedded ontologists
- On-prem, VPC and air-gapped deployment options
- SSO, SAML, audit logs and SIEM export on Production Deployment
- Multi-workspace and multi-ontology management
About Lettria
Lettria is a Graph Context Layer that sits between your enterprise data and the AI systems reading it. It converts documents, native tables, layouts and SAP exports into ontology-powered knowledge graphs, then answers questions through graph retrieval rather than vector similarity. The company's framing is direct: vectors retrieve, graphs reason. You enter through one of two products. Knowledge Studio is the graph-native document-intelligence platform for knowledge and business teams in regulated industries. Perseus is the developer-facing infrastructure for generating ontologies and building versioned knowledge graphs with business experts in the loop. Three capabilities carry the platform. Automated ontology building generates production-grade ontologies from your domain data in days rather than months, governed and version-controlled. Text-to-Graph turns messy documents, tables and SAP exports into a clean queryable graph, with Lettria claiming 30%+ more accuracy and up to 400x faster processing than general LLMs. Graph Retrieval returns full provenance on every answer, so each result traces back to its source documents and the reasoning path behind it. Lettria publishes benchmark comparisons against vector agents: 90%+ multi-hop reasoning, 100% numerical reasoning, 83% temporal reasoning, and 81.7% overall accuracy versus 57.5% for vectors. Production case studies include Alfa Laval (+30% extraction accuracy across thousands of manuals), Wisecube (108 GB of biomedical text structured, 500+ classes monitored), Leroy Merlin (10,000+ product classes), and AP-HP (+60% faster research). This is aimed at finance, healthcare, legal and engineering organizations that must defend an AI answer to a regulator, auditor or domain expert. It is not a weekend chatbot project — it is a knowledge-engineering commitment that expects domain experts to help define concepts and relationships.
Behind the Verdict
Lettria's central argument is that vector search hits a ceiling the moment an AI has to connect several sources, reason over structured business data, and produce an answer someone can verify. That is a real ceiling, and the benchmark table on the homepage is more specific than most vendors publish: multi-hop reasoning 90%+ versus low for vector agents, numerical reasoning 100% versus low, temporal reasoning 83% versus 50%, overall accuracy 81.7% versus 57.5%. You can read the full methodology behind it. Strengths. The ontology layer is the differentiator, and Lettria automates the part that used to kill graph projects — building the ontology. The claim is days, not months, from auto-generation, and the result is governed and version-controlled rather than hand-modeled and drifting. Text-to-Graph handles native tables, layouts and multimodal content, and unifies structured and unstructured sources in one graph, which is where most RAG stacks quietly fall apart. Graph Retrieval returns full provenance with each answer, meaning documents plus reasoning path — that is what makes an answer defensible to a regulator. Deployments are real: Alfa Laval reports +30% extraction accuracy over thousands of manuals and specs, Wisecube structured 108 GB of biomedical text monitoring 500+ classes, AP-HP reports +60% faster research, Leroy Merlin unified 10,000+ product classes. On security, Lettria states SOC 2 Type II, GDPR with DPA, ISO 27001, with HIPAA-ready configuration and EU AI Act-aligned controls on Enterprise and above. Data stays in your chosen region (EU, US, or custom) and Lettria states your documents, ontology and queries are never used to train its models or third-party models. Weaknesses and honest friction. This is not plug-and-play. You have to model your domain, and you have to lend real subject-matter experts to the ontology work — Lettria's own materials say the ontologists work embedded with your SMEs. Real-time conversational use cases that need minimal latency are a poor fit. The GraphRAG side is scoped, not self-serve: every engagement opens with a scoped pilot and ontology plan, priced by contacting the team, with annual invoicing against a PO as the default and multi-year and custom cycles on Enterprise. Perseus changes that picture for developers — it's free to start, with Plus at $0.01 per PCU pay-per-use. Sub-enterprise teams that want a chatbot over a folder of PDFs should look elsewhere. Where it fits. Regulated finance, healthcare, legal and industrial engineering teams whose answers must be traceable and whose data is genuinely high-value. Where it doesn't: latency-sensitive chat, teams unwilling to invest in ontology modeling, and organizations with no domain experts to spare.
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Real-world workflow fit
Concrete scenarios for the personas Lettria actually fits — and what changes day-one when you adopt it.
You start with a scoped Pilot Program against one use case — say, structuring clinical trial documentation — over 8–12 weeks with an embedded ontologist and a quarterly ontology refresh.
Outcome: You reach proven value on a single use case with a clear ontology plan, and pilot fees credit against the first year's license if you convert to Enterprise.
You sign up for Perseus free, open the Ontology Editor, auto-generate an ontology from domain data within the 30 KB input cap, and run up to 5 graph builds in 30 days.
Outcome: You have a versioned ontology and graph you can evaluate before anyone signs a contract, then move to Plus at $0.01 per PCU when volume grows.
You deploy Production Deployment across multiple workspaces and ontologies, connect document ingestion into your cloud or VPC, and enable SSO, SAML, audit logs and SIEM export.
Outcome: Every answer returns the source documents and the reasoning path behind it, and you can hand an auditor the provenance trail rather than reconstructing it by hand.
Use Cases
- Analyze clinical trial and biomedical literature with full traceability back to source documents.
- Extract and organize financial, regulatory and ESG disclosures for auditable insights.
- Review legal contracts with reduced manual cross-checking.
- Resolve engineering issues faster by querying unified technical documentation.
- Unify a sprawling product catalog into one machine-readable taxonomy.
- Build ontology-driven assistants for specialized regulated domains.
- Structure biomedical research into traceable knowledge for safer clinical AI.
- Enrich CRM records with ontology-based classification.
Models Under the Hood
as of 2026-09-27
Limitations
- Lettria expects an ontology-driven approach — you model your domain, and that is not as plug-and-play as vector search over a folder of PDFs.
- You will need domain experts involved: the ontology sprint and graph build are run with your subject-matter experts, and Lettria's ontologists work embedded with them.
- Real-time conversational use cases that need minimal latency are a poor fit.
- The GraphRAG side is scoped rather than self-serve — each engagement starts with a scoped pilot and ontology plan, with annual invoicing against a PO as the default.
- Perseus, the developer platform, is a separate product with its own free tier and per-PCU Plus plan, and you do not interact with it directly when running the Lettria platform.
- Compliance features that security-conscious buyers ask about — HIPAA-ready configuration and EU AI Act-aligned controls — are on Enterprise and above.
as of 2026-10-05
Verification history
We have re-verified Lettria 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Lettria tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0
Ideal for
An individual developer or a small side project testing whether graph-based ontology building fits the problem.
What this tier adds
Starting tier on Perseus: Ontology Editor and Evaluation, 30 KB max input size, 5 graph builds and 1 ontology build per 30 days, community support.
Plus
$0.01 per PCU
Ideal for
Small teams and growing projects that have outgrown the free build caps and need real input sizes.
What this tier adds
Raises the input cap to 1 MB and the limits to 3000 graph builds and 300 ontology builds per 30 days, with email support, billed at $0.01 per PCU.
Enterprise
Custom
Ideal for
Large organizations needing unlimited throughput, dedicated support and a 99.99% uptime SLA.
What this tier adds
Unlimited input sizes and unlimited graph and ontology builds, dedicated support, and a 99.99% uptime SLA; priced by contacting sales.
Where the pricing makes sense
The company stage and team size where Lettria's pricing actually pencils out — and where peers do it cheaper.
Lettria splits into two commercial paths. Perseus, the developer platform, is free to start (30 KB max input, 5 graph builds and 1 ontology build per 30 days) with Plus at $0.01 per PCU for small teams. GraphRAG is scoped enterprise pricing — Pilot Program, Production Deployment, and In-house Experts, all quoted. That puts it above self-serve vector RAG tools and in the same conversation as Palantir or a bespoke consulting engagement.
Setup time & first value
How long it actually takes to get something useful out of Lettria — broken out by persona, not the marketing-page minute.
Perseus free: minutes to an ontology draft via the Ontology Editor, constrained by the 30 KB input cap. Perseus Plus: same editor with 1 MB inputs and 3000 graph builds per 30 days, so first real graph within days. GraphRAG Pilot Program: a two-week ontology sprint opens a typical engagement, then graph build runs 6–12 weeks before ongoing refresh cycles.
Switching to or from Lettria
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From vector RAG stacks: run a scoped pilot on one use case; Lettria's benchmarks put graph agents at 81.7% overall accuracy versus 57.5% for vector agents, with multi-hop at 90%+ versus low.
- →From hand-built knowledge graphs: Perseus auto-generates production-grade ontologies from your domain data in days rather than months, then governs and versions them.
- →From spreadsheets and SAP exports: Text-to-Graph unifies structured and unstructured sources into a single queryable graph, so manual field-mapping work drops away.
- →From a document-heavy manual review process: Alfa Laval unified thousands of manuals and specs into one graph and reported +30% extraction accuracy.
- ↗To a vector-first RAG stack: rebuild your retrieval as embedding search plus a vector database, and accept the multi-hop and numerical reasoning gap Lettria publishes against.
- ↗To an umbrella data platform: if you want ingestion, cataloging and BI alongside graph reasoning, a broader platform vendor covers more ground at the cost of the ontology-specific depth.
- ↗To in-house graph engineering: Lettria's ontologists transfer knowledge during engagements so you are not locked in, and Perseus gives SDK-level access if your team wants to build and own the graphs.
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Lettria”, and we withheld 6: 6 could not be judged, because “Lettria” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Lettria.
Official links
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Featured Head-to-Head Comparisons
Lettria vs Screenplayiq
ScreenplayIQ and Lettria serve entirely different domains: ScreenplayIQ is for film industry professionals needing data-driven script feedback and financial forecasts, while Lettria is for regulated industries requiring traceable, ontology-based document intelligence. Choose ScreenplayIQ if you're a screenwriter or producer evaluating a feature film's market potential; choose Lettria if your team needs secure, verifiable NLP for complex documents in healthcare, finance, or legal. They are not direct competitors.
Lettria vs Codametrix
If your goal is automated medical coding at enterprise scale with proven denial reduction and a 5:1 ROI, CodaMetrix is purpose-built and KLAS-ranked #1. For broader document intelligence — parsing complex PDFs, building knowledge graphs, or deploying private chatbots with full traceability — Lettria offers a flexible no-code platform. Choose based on your primary need: coding automation vs. open-ended document analysis.
Lettria vs Isomorphic Labs
Isomorphic Labs and Lettria serve completely different markets. Isomorphic Labs is a high-stakes AI drug discovery partner for big pharma, requiring deep collaboration and massive capital—not a tool you buy. Lettria is a no-code document intelligence platform for regulated enterprises needing traceable, ontology-driven extraction. Choose Lettria if you need trustworthy NLP for compliance-heavy documents; choose Isomorphic Labs only if you are a pharma giant seeking a transformative AI R&D partner.
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