Enterprise RAG · Document AI · Knowledge assistants
Build AI-powered knowledge systems that find reliable answers, extract information from documents, and connect knowledge to business processes. DEIENAMI engineers enterprise RAG, document intelligence and knowledge assistants with source citations, access controls, evaluation and human oversight.
Feasibility reviews check data, access and fit. Proof-of-value and production work are quoted after qualification. This page is not a public price list.
Business problems
Three jobs buyers actually fund. Pick the lane that matches your week.
Knowledge intelligence
Interactive examples
Illustrative demos with sample data only. No live model calls. No client documents.
Knowledge assistant · illustrative
Answer
Source preview
Document processing · illustrative invoice
INV-10482 · Acme Components Ltd
Scanned PDF sample. Layout-aware extraction applied.
Extracted fields
Validation: exception. Human review required before write-back.
What DEIENAMI builds
Nine concrete outcomes. We start with one use case, not a vague platform rewrite.
Retrieval that respects permissions and returns answers staff can check against sources.
Natural-language access to policies, manuals and support knowledge inside your access rules.
Structured fields from invoices, forms and reports with validation before anyone trusts the row.
Route incoming documents to the right queue, owner and retention path.
Find procedures and specs across engineering libraries without guessing which folder holds the truth.
Surface clauses, dates and obligations with clear pointers back to the signed text.
Connect CRM, ERP, ticket systems and document stores so retrieval uses the live estate.
Measure groundedness, citation quality and failure modes before you scale usage.
Move extracted data through approval and into the systems that run the business.
Engineering evidence
Labelled samples of assessment and delivery outputs. RAG is not a chatbot bolted onto a vector database.
Sample
Parse, chunk, index and retain ACL metadata for the agreed corpus.
Sample
Hybrid retrieval, grounding, citations and abstain behaviour.
Sample
Identity and ACL filtering before documents enter the answer path.
Sample
Extract, validate, human review and controlled write-back.
Sample
Groundedness checks, citation quality and known failure cases for the scoped questions.
Sample
Runbook, monitoring view, ownership and what was not tested.
Technical considerations we design explicitly: preprocessing, OCR where needed, retrieval and reranking, source attribution, authorization, groundedness evaluation, error handling, monitoring, and cost or latency controls.
How engagement works
Scope boundaries for evaluation. Final terms follow qualification. No unapproved public pricing.
Assess
Qualify the problem
Concise review for suitable enquiries. Not unpaid consulting.
Request an AI Feasibility ReviewProve
Quoted after qualification
Indicative duration often a few weeks, subject to feasibility. Timing is confirmed after qualification.
Discuss a proof of valueDeploy
Scoped after Prove
Complex custom APIs and multi-system programmes are scoped separately.
Discuss deploymentOperate
Separate agreement
No unlimited support. No uptime guarantee on this page.
Discuss careStraight answers on ChatGPT, accuracy, security, systems and ownership. The contract still sets the exact terms.
A general assistant can be enough for low-risk drafting with public or non-sensitive material. Engineered software is justified when you need organisation-specific access controls, application integration, citation evaluation, operational ownership and a clear abstain path.
Yes. RAG reduces some classes of error but does not remove them. We design evaluation, citation inspection, abstention and human review so risk is visible and managed for the scoped use case.
Access-control design, approved infrastructure choices, data handling rules and security testing are part of implementation planning. We do not claim a regulation is met unless the engagement and evidence support that claim.
Usually when APIs, files or approved connectors can supply the corpus and write-back path. Feasibility is confirmed in Assess. Integration constraints are scoped explicitly. See also System and API integration.
Indexing updates, version handling, monitoring and freshness requirements are designed into Operate. Stale sources are treated as an operational risk, not a surprise.
Yes. Prove is built for one business use case, limited sources and defined evaluation questions before any wider rollout.
Ownership, licensing, source code and support terms are defined in the contract for the engagement. Nothing on this page overrides that agreement.
Qualified enquiry
Tell us where your business knowledge lives and what your team needs to accomplish. We will review the opportunity and recommend an appropriate next step. Your submission creates a New lead, not an automatic Qualified opportunity.
Your enquiry is logged as a New lead. A person will triage it. This is not yet a Qualified opportunity.
