RAG Development & AI Data Infrastructure
Production AI needs more than a model call. We build the ingestion, extraction, retrieval, evaluation, monitoring, and feedback loops that keep workflows accurate as documents, teams, and business rules change.
The operating layer around the model
Best suited toTeams with a valuable corpus or recurring document flow that needs reliable ingestion, permissions, retrieval, evaluation, and ongoing maintenance.
Not designed forA vector database installed without a defined user workflow, source ownership, quality bar, or plan for stale and conflicting information.
AI is only as good as the system around it.
Document data layer
We organise the material your AI workflow depends on: source records, document versions, extracted fields, citations, review decisions, business rules and downstream outputs.
The data layer is built for retrieval, auditability, and repeat runs, so every result can be traced back to the document and context that produced it.
Extraction and validation pipelines
We build pipelines that turn PDFs, drawings, spreadsheets, emails, exported reports, and system records into structured workflow data.
Validation checks catch missing fields, impossible values, low-confidence extraction, stale versions, and cases that require human review before they affect operations.
Evaluation, monitoring and feedback
A live system needs a scorecard. We define test sets, expected outputs, reviewer override tracking, regression checks and alerts for workflow failures.
When the workflow degrades, the team can see why: document quality, prompt drift, rule changes, integration failures, or missing training examples.
Managed improvement
After deployment, we can stay close to the system: review failures, improve prompts and extractors, add document types, tune workflows, and report performance against business metrics.
Each change is tested against the same evaluation set before release, so you can see whether it helped.
What the engagement includes
This service builds the source, retrieval, validation, evaluation, and monitoring systems that keep AI useful after a demonstration. It is suited to document-heavy workflows where provenance and change matter as much as model output.
Delivery process
- 01
Corpus and source audit
We identify systems of record, document families, versions, permissions, update frequency, metadata quality, and the questions or tasks the infrastructure must support.
- 02
Ingestion and retrieval design
Parsing, chunking, metadata, indexing, access controls, source citations, and retrieval strategies are selected against representative material rather than generic benchmarks.
- 03
Evaluation and observability
Known questions, expected sources, extraction fields, failure examples, latency, cost, reviewer feedback, and regression checks become an operating scorecard.
- 04
Managed improvement
New document types, source changes, failed cases, and reviewer decisions feed a controlled improvement loop with versioned changes and measurable releases.
Deliverables
- Source and data-readiness assessment
- Ingestion, extraction, retrieval, and permission architecture
- Traceable source metadata and citation behaviour
- Evaluation dataset, monitoring views, and regression checks
- Runbook for source updates, incidents, and ongoing improvement
Measurement and engagement shape
We measure the infrastructure against the task it supports: source recall, citation correctness, extraction quality, unanswered questions, freshness, latency, cost, and reviewer acceptance. A retrieval score alone is not a business outcome.
The engagement can support a new product or strengthen an existing deployment. Scope depends on corpus size, file diversity, system access, permission complexity, freshness requirements, and the evaluation standard expected by operators.
Evidence and preparation
Questions about RAG Development & AI Data Infrastructure
What happens after launch?
We can keep running the system with you: data pipelines, evaluation, monitoring, failure review, tuning, new document types, and regular reporting against the outcome you agreed at the start.
Why does document AI need ongoing infrastructure?
Documents, templates, teams, rules, and edge cases change. Without versioning, validation, evals, and feedback loops, an impressive prototype can quietly degrade inside real operations.
Can you operate on top of our existing systems?
Yes. We design the data layer around existing document stores, spreadsheets, databases, APIs, and workflow tools rather than forcing a full platform migration at the start.
Go deeper with the free course:
Enterprise RAG on Your Own InfrastructureOther services
- AI Agent Development ServicesAgents that read files, extract facts, draft outputs, work with your systems and pass uncertain cases to a person.
- AI Strategy & RoadmappingFind out where AI is worth using in your work, what is technically realistic, and what to test first.
- Rapid AI MVPA 28-day sprint that proves one AI workflow on your own files before you commit to a larger build.
Start with one workflow.
Tell us which piece of work you’d like AI to take on. We’ll reply with what a system could do, what it should leave to a person, and what a first version would take to build.