Service / 02 Build
Enterprise RAG & Knowledge AI
Retrieval-augmented systems that answer from your content with citations, permissions and measured quality.
/ 01The business problem
Why this matters
Most disappointing knowledge assistants fail at retrieval, not generation. Documents are split badly, permissions are ignored, tables and scanned pages are lost, and nobody measures whether the right passage was found.
We treat RAG as an information retrieval problem with a language model on top. That means careful ingestion, hybrid search, re-ranking, access control and an evaluation set built from real questions.
Who it is for
- Knowledge management and intranet owners
- Support and service organisations
- Professional services firms with large document estates
/ 02Scope
What the engagement covers
- 01Source inventory and content quality review
- 02Ingestion with structure, metadata and permissions
- 03Hybrid retrieval, re-ranking and filtering
- 04Answer generation with citations and abstention
- 05Evaluation set and scoring pipeline
- 06User interface and feedback capture
/ 03Technical approach
How we work on it
Questions before indexes
We collect real questions and expected sources first, which defines what good retrieval means for you.
Engineer the chunks
Splitting respects headings, tables and document structure, and each passage keeps the context needed to understand it.
Measure each stage
Retrieval recall and answer faithfulness are scored separately so we fix the right component.
/ 04Process
From first call to production
- 1
Consultation
A focused conversation about the problem, constraints and what success would mean.
- 2
Discovery
Evidence gathering: data samples, process walkthroughs and a measured baseline.
- 3
Proposal
Scope, approach, team, timeline, risks and a fixed or estimated price.
- 4
Delivery
Iterative sprints with demos and evaluation results at each milestone.
- 5
Handover or run
Documentation, runbooks and training, or ongoing operation by our team.
/ 05Deliverables
What you receive
- Ingestion pipelines and connectors
- Search index with permission filtering
- Answer service with citations
- Evaluation dataset and reports
- Front end for web, Teams or Slack
- Operations runbook
Engagement options
Fixed-scope sprint
Two to six weeks with a defined deliverable and price, suitable for discovery, assessments and prototypes.
Delivery team
A cross-functional squad for a build or integration, planned in milestones with demos and evaluation results at each one.
/ 06FAQ
Common questions
Do we need a vector database?
Often a search engine or database you already run can handle vector and keyword search together. We choose based on scale, latency and operations, not fashion.
Should we fine-tune a model on our documents instead?
Usually not for factual recall. Fine-tuning changes behaviour and style; retrieval is better for facts that change and need citations.
Related solutions
- SolutionInternal Knowledge SearchAnswers from policies, documentation and past work, with citations and permissions intact.
- SolutionCustomer Support & CXFaster, more consistent service: AI triage, agent assist and self-service that hands over cleanly.
- SolutionIT & EngineeringService desk automation, incident assistance and engineering knowledge, connected to your tooling.
Related product concepts
Talk to an engineer about enterprise rag & knowledge ai
Bring the problem and any constraints. You will leave the conversation with an honest view of the options, even if the answer is not us.