Parsing
Tables flattened into run-on text, scanned pages skipped, and headers repeated in every chunk.
Layout-aware parsing with OCR, table structure, and reading order preserved.
Enterprise AI, grounded in your business context.
We design and build the RAG systems behind enterprise AI: document pipelines built for millions of pages, retrieval tuned to your corpus, and permission-aware business context behind every answer and every agent.
74M+
vectors indexed
112B+
tokens handled
150K+
businesses building on Cody
87
countries
Why enterprise AI disappoints
The model is rarely the problem. The context is.
Most stalled AI pilots run a capable model on the wrong evidence. Answer quality is decided upstream, in how documents are parsed, chunked, indexed, filtered, and assembled into context. That layer is our specialty.
StageWhere it breaksHow we engineer it
Chunking
A fixed-size split separates a policy clause from the exception that changes its meaning.
Structure-aware chunks that follow sections and carry their document and heading path.
Retrieval
Vector-only search misses part numbers, SKUs, acronyms, and exact policy names.
Hybrid keyword and semantic retrieval, fused and reranked for your domain.
Permissions
The assistant quotes a document the person asking was never allowed to open.
Source-system permissions synced into the index and enforced on every query.
Freshness
Last quarter’s price list confidently answers this quarter’s question.
Incremental sync, versioning, and effective dates keep the index in step with the source of truth.
Evaluation
Nobody can say whether the latest prompt or model change made answers better or worse.
Golden question sets and regression evaluations gate every change before release.
Reference architecture
Anatomy of a production RAG system.
Every engagement starts from our reference architecture, then each stage is tuned to your documents, systems, languages, and risk profile. Nothing is a black box: every answer comes with a trace you can inspect.
- 01
Connect
Ingest from where knowledge already lives, with scheduled and event-driven sync from every source.
- File shares & drives
- Wikis & intranets
- Ticketing & CRM
- Databases & APIs
- 02
Parse
Convert PDFs, scans, slides, spreadsheets, email, and web pages into clean, structured text without losing tables or reading order.
- Layout analysis
- OCR
- Table extraction
- Metadata capture
- 03
Chunk & enrich
Split along the document’s own structure, attach titles and section paths, tag entities and dates, and drop near-duplicates before anything is embedded.
- Structure-aware chunking
- Parent–child context
- Entity tagging
- Deduplication
- 04
Embed & index
Choose embedding models by language and domain, then index vectors alongside keyword fields, metadata, and access-control lists.
- Dense + sparse
- Metadata filters
- ACL fields
- Versioned indexes
- 05
Retrieve & rerank
Rewrite and decompose the question, run hybrid search, filter by permission, and rerank so only the strongest evidence survives.
- Query rewriting
- Hybrid search
- Rank fusion
- Cross-encoder reranking
- 06
Generate & verify
Assemble context within a strict token budget, answer with citations, check each claim against its source, and abstain or escalate when evidence is thin.
- Token budgeting
- Inline citations
- Claim verification
- Abstain or escalate
Retrieval trace
One answer, end to end
Illustrative922 ms
Can our field technicians replace the X200 valve under the 2025 service agreement?
Field technicianEMEAService tier 2
Rewrite & decompose
3 sub-queries
38 msHybrid search
keyword + vector · 240 candidates
71 msCRM lookup
service contract SA-2025-118
118 msPermission filter
212 of 240 visible to this user
5 msRerank
cross-encoder · top 8 kept
94 msAssemble context
8 passages + 1 record · 3,840 tokens
9 msGenerate
grounded answer · 3 citations
652 msVerify claims
3 of 3 supported
53 ms
Grounded answer
Yes. Clause 7.2 of the 2025 agreement covers on-site valve replacement by certified technicians.1 Use torque spec TS-14 from the X200 procedure.2 Log the part swap on the work order.3
- 1Service Agreement 2025 · p. 14
- 2X200 Maintenance Manual · §4.3
- 3Field Operations SOP v12 · p. 3
Observed end to end. Every stage is traced and measured, from retrieval recall and groundedness to latency and cost per answer, so improvements are proven rather than assumed.
Context engineering
Documents are only half the context.
A correct answer depends on who is asking, what is true right now, and what your business means by its own words. We engineer all of it into a context window the model can actually use.
Context window
What the model sees for one answer
- Instructions & policy800
- Who is asking150
- Conversation state550
- Retrieved evidence3,600
- Live business records1,000
- Glossary & entities300
- Reserved for the answer1,600
Illustrative allocation. Budgets are tuned per model, language, and use case, and every token has to earn its place.
Who is asking
Role, team, region, and entitlements decide what may be retrieved and how the answer should be framed.
What the business knows
Policies, manuals, contracts, and past resolutions, retrieved as evidence and cited back to the passage.
What is true right now
Live records from CRM, ERP, ticketing, and order systems, fetched through tools at question time instead of embedded and left to go stale.
How your business talks
Glossaries, product names, acronyms, and entity resolution, so “X200”, “X-200 Pro”, and “the 200 valve” resolve to the same part.
What must never happen
Policies, refusal rules, and escalation paths applied before the model writes a single word.
Engineered for scale
Built for millions of documents, not a demo folder.
A pilot over 200 PDFs proves very little. Production means continuous ingestion, re-indexing without downtime, and latency and cost that stay predictable as your corpus and usage grow. Across the Cody platform, we already index more than 74 million vectors.
Incremental ingestion
Change detection reprocesses only what changed, and deletions propagate so retired content stops answering.
Zero-downtime re-indexing
New embedding model or chunking strategy? We build the new index alongside the live one, evaluate it, then switch traffic.
Latency budgets
Every stage has a time budget. Caching, parallel retrieval, and streaming keep answers fast as the corpus grows.
Cost per answer
Embedding dimensions, index compression, caching, and model routing are tuned so unit costs stay predictable at volume.
Multilingual retrieval
Cross-lingual embeddings and language-aware keyword analysis, so a question in Spanish finds the policy written in English.
Isolation and residency
Separate indexes or filtered namespaces per business unit, region, or customer, matched to your data-residency requirements.
Measured and governed
Measured before launch. Traceable after.
Quality and governance are engineering work, not a review at the end. We build the evaluation harness and the controls alongside the system, so owners can see how it performs and why it answered.
Evaluation harness built in
Evaluate
Retrieval quality
Recall@k, MRR, and nDCG measured on golden question sets written with your subject-matter experts.
Evaluate
Answer quality
Groundedness, citation accuracy, and completeness scored automatically, calibrated against human review.
Evaluate
Regression gates
Every change to prompts, models, chunking, or indexes reruns the suite before it reaches users.
Govern
Permission-aware retrieval
Access rules from each source system enforced at query time, with a log of what was retrieved for whom.
Govern
End-to-end traceability
Each answer keeps its query, passages, index and prompt versions, and model, so reviewers can reconstruct exactly what happened.
Govern
Human review and escalation
Confidence thresholds route weak-evidence questions to people, with clear fallback behavior.
How we work
From corpus audit to production, one measured step at a time.
Every step produces evidence you can review: corpus profiles, retrieval baselines, evaluation scores, and a costed production plan.
01
Audit the corpus
We profile your sources for format, volume, quality, duplication, permissions, and freshness, and map the governance boundaries the system must respect.
02
Baseline retrieval
With your experts we build a golden question set, then measure how well your content can be retrieved before any model writes an answer.
03
Pilot on real work
A working system on a representative slice of the corpus, used by real people, with traces and evaluation scores you can inspect.
04
Scale to production
Full ingestion, monitoring, cost controls, and an operating model with a clear owner for every part of the system.
Solution Design Study
Explore the Solution Design StudyTurn the approach into a decision-ready plan.
When the opportunity is clear but the right system is not, a bounded Solution Design Study tests your documents, retrieval quality, controls, and costs before you commit to a build.
01
Corpus and retrieval assessment
Source profiles, retrieval baselines, and the gaps that matter most.
02
Reference architecture
Parsing, indexing, retrieval, models, integrations, and deployment choices.
03
Working proof on your data
A representative experience with traces and evaluation scores.
04
Control and rollout plan
Risks, owners, evidence, costs, and a practical path to production.
From our research
View all articlesWe publish what we learn.
Long-form, vendor-neutral deep dives on the parts of RAG that decide answer quality: parsing, embeddings, vector databases, and cost.
Document parsing
What Is Docling? A Practical Guide to Document Parsing for RAG
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Read articleVector databasesTop 8 Vector Databases for RAG in 2026
Compare eight leading vector databases for RAG by hybrid search, filtering, deployment, multitenancy, and production fit—plus a practical selection framework.
Read articleIndexing costHow Much Does It Cost to Vectorize a Database for RAG?
Learn what it really costs to vectorize a database for RAG, from embeddings and parsing to vector storage, queries, replicas, and re-indexing.
Read articleParsing modelsCohere Parse v5.0: Why Better RAG Starts With Better Documents
Cohere Parse v5.0 turns PDFs, slides, tables, forms, and images into RAG-ready Markdown. Here is what makes it different, where it fits, and what its limits mean for enterprise search.
Read articleTalk to the enterprise team
Bring us your hardest documents.
Share the corpus, the systems, and the questions that matter. We will tell you what accuracy is realistic, what it takes to get there, and whether a Solution Design Study is the right first step.
An honest read on your corpus and retrieval quality
Architecture shaped around your systems and permissions
A pilot measured against your real questions