LLM and RAG Integration
Answers from your own knowledge, with the source attached.
AI that answers from your own documents and data, with sources shown.
The problem
Why this matters
General AI models do not know your policies, products or contracts, and they guess when unsure. Retrieval-augmented generation (RAG) makes the model answer from your documents and show where each answer came from.
Large language models write fluently but know nothing about your business. Retrieval-augmented generation (RAG) fixes that: before answering, the system finds the relevant passages in your own documents and data, and the model answers from those.
The quality of a RAG system depends less on the model than on the data work around it: how documents are split, searched, ranked and kept up to date. That is where we spend most of our effort.
How it works
How it works, step by step
- 1
Collect
We connect your documents, wikis, tickets and databases, and keep them in sync.
- 2
Index
Content is split into searchable pieces and stored with access rules attached.
- 3
Retrieve
For each question, the most relevant pieces are found and ranked.
- 4
Answer with sources
The model answers only from what was retrieved, and shows where each part came from.
What we build
LLM and RAG Integration: what is included
- 01
Knowledge assistants for staff and customers
- 02
Document search across files, wikis and tickets
- 03
AI features inside your existing product
- 04
Structured data extraction from documents
- 05
Model selection, fine-tuning and cost optimization
Use cases
Where it is used
- Policy and procedure questions
- Product and technical support
- Contract and document review
- Sales enablement
- Onboarding and training
- Analytics in plain language
What you receive
- The assistant or API
- A data pipeline that keeps content current
- Accuracy results on your test questions
- Access controls that mirror your permissions
- Documentation and source code
Technology
- Vector databases
- Hybrid search
- Re-ranking
- LlamaIndex
- LangChain
- Commercial and self-hosted models
How the work runs
A clear path with a decision point at every step
- 01
Discover
A free call to understand your goal, users, data and constraints, followed by a short written summary.
- 02
Assess
A review of your data, systems and any existing prototype, ending in a plan with scope, price and timeline.
- 03
Pilot
One workflow built end to end and released to real users.
- 04
Build
The full system, delivered in two-week cycles with a demo at the end of each.
- 05
Run
Monitoring, support, upgrades and steady improvement on a monthly plan.
Pricing
Clear scopes. Starting prices you can plan around.
Discovery call
Free
30 minutes
A first look at your idea or system and a clear next step.
Readiness Assessment
From $3,000
1 to 2 weeks
A review of your data, systems and any prototype, ending in a fixed-price plan. Credited in full if you build with us within 60 days.
AI Pilot
From $8,000
4 to 6 weeks
One workflow built end to end and live with real users.
AI Production Build
From $18,000
10 to 18 weeks
A complete AI system in production, in your accounts.
Software Build
From $12,000
6 to 14 weeks
Web, mobile and backend products, starting with an assessment.
Managed Services
From $2,000 a month
3-month minimum
Monitoring, fixes, upgrades and improvement after launch.
- Prices in US dollars. A fixed quote follows the assessment.
- AI model and cloud costs are billed to your own accounts, and we estimate them up front.
Questions
What buyers ask us
Will the AI make things up?
We reduce this by grounding answers in your sources, showing citations, and having it say when it does not know. We measure the rate before launch.
Can it respect who is allowed to see what?
Yes. Retrieval follows the access rules in your source systems.
What about documents that change?
The pipeline re-indexes on a schedule or on each update.
Do we need to move our data somewhere new?
No. The system reads from where your data already lives, and the index can run in your own cloud account.
Can it work in more than one language?
Yes. Questions and documents can be in different languages, and we test accuracy for each one you use.
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