RAG on Company Documents
Ask your documents. Get cited answers inside your permissions.
We build retrieval-augmented generation systems that let staff ask questions of policies, contracts, manuals and tickets and get answers with citations, respecting who is allowed to see what.
- Cited answers
- Permission-aware
- Your cloud
Free 30-minute call with a senior specialist. Reply within one business day. Based in Jaipur, working with businesses across India.
- Discovery callFree 30 minutes with a senior specialist
- Scoped first phaseWritten scope, timeline and fixed price
- Build with weekly demosWorking software you can see every week
- Launch and supportGo-live with your team ready, then ongoing support
What is a RAG chatbot?
A RAG chatbot uses retrieval-augmented generation: it searches your own documents for relevant passages and has a language model answer from them, with citations. Stack18 builds RAG systems that respect document permissions, run in your cloud or an approved environment and are measured for accuracy.
Last reviewed 30 October 2026 by the Stack18 delivery team in Jaipur.
You need this when…
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RAG on Company Documents, end to end.
Each capability is delivered by the same team on the Agentic SDLC, with your approval at every critical step.
Ingestion and indexing
Bring documents in and keep them current.
- PDF, Office, wiki and ticket sources
- Scheduled re-indexing
- Versioning and source tracking
Retrieval quality
Find the right passage first.
- Hybrid search and re-ranking
- Chunking tuned to your content
- Citation to the source page
Permissions and privacy
Only what each person may see.
- Role-aware retrieval
- PII redaction
- Private model or approved API
Evaluation and guardrails
Know it is right before rollout.
- Test question sets
- Accuracy and refusal scoring
- Out-of-scope refusal
Interfaces and integration
Where your team already works.
- Web, Slack or Teams access
- Helpdesk and CRM embedding
- Usage and cost dashboards
A typical engagement, step by step.
Senior people lead every phase. Specialised AI agents on the Agentic SDLC handle the repetitive work, and nothing ships without your approval.
Discovery
Map how private knowledge AI works today, the systems and data involved, and agree the first measurable outcome.
Design
Design the workflows, roles, integrations and approval points for private knowledge AI.
Build in weekly demos
Your team sees working private knowledge AI software every week, with changes agreed in the open.
Launch and support
Data migration, training, go-live support and a measured 30-day review against the baseline.
Start small. Scale when it works.
Every engagement begins with a short, fixed-price step so you can judge the fit before committing further. See pricing.
Discovery Sprint
Teams deciding scope and priorities before they commit.
- Duration
- 2 weeks
- Pricing
- Fixed price
- Scoped plan and timeline
- Team and fixed price
- Process map or prototype
AI Enterprise OS Launch
Teams that want a proven base they can extend.
- Duration
- Launch edition, live in weeks
- Pricing
- From ₹3.2L, one-time, no per-user fees
- Pre-built modules to configure
- Data migration and training
- Extend with custom work later
Transformation Pod
Multi-system programmes that run for years.
- Duration
- 12–36 months
- Pricing
- Annual contract
- Dedicated cross-functional team
- Quarterly business reviews
- One accountable partner
What we have shipped, and what a typical engagement looks like.
Delivered projects link to the full case study. Illustrative scenarios describe typical engagements and are not named client results.
Services company with a large policy library
- Challenge
- Employees ask HR and IT the same questions; policies are scattered across drives.
- What we do
- Permission-aware RAG assistant with citations and a measured test set.
First version in weeks
Free private knowledge AI review
Tell us where private knowledge AI hurts most. We map what to fix first, whether ready-made or custom fits, and a realistic first step.
- Where to start and why
- Ready-made vs custom view
- Indicative timeline
We agree targets before we start.
Every engagement has a baseline and a target on the measures that matter to you, reported at every read-out.
RAG on Company Documents questions.
Will our data train public models?
We design so your documents are not used to train public models, and agree the model provider and data terms in writing before build.
How do you stop wrong answers?
We ground answers in retrieved text, require citations, refuse out-of-scope questions and measure accuracy on a test set built from your real questions.
Can it respect who can see which document?
Yes. Retrieval can be filtered by user role or group so answers draw only on documents that person may access.
How many documents can it handle?
Scale depends on formats and quality rather than a fixed count. We size and test this on a sample of your content during discovery.
How soon can we see it working?
A first working version on a sample of your documents is typically shown within the first weeks, then hardened for production with evaluation and permissions.
Let's scope your RAG on Company Documents project.
Tell us where you are and where you want to be. A senior specialist will reply within one business day with next steps.
- Free 30-minute consultation
- Indicative estimate after the call
- NDA available on request