Document & Knowledge Intelligence
Your best documentation is trapped in your worst search tool.
Enterprise RAG implementation and AI knowledge assistants, built in your cloud.
AI that reads your SOPs, contracts, policies, and internal docs — and answers with citations. Permission-aware. Deployed in your VPC or on-prem. Zero hallucination tolerance for compliance teams.
Answer
Enterprise contracts are eligible for a pro-rated refund within 30 days of the billing cycle, subject to the terms in Section 8.3...
Cited from: Enterprise Agreement v2.4, §8.3
Sources
85–95%
Answer accuracy
100%
Citation-backed
Your cloud
VPC / on-prem
The problem
80% of your business data is unstructured — SOPs, contracts, policies, playbooks, meeting notes, tribal Slack threads. Your team spends roughly 20% of their week searching through it.
- New hires take 3–6 months to ramp because no one can find anything
- Contract teams spend hours locating clauses across thousands of agreements
- Support teams answer the same questions with conflicting answers
- Employees paste confidential docs into ChatGPT because your intranet search is unusable
Enterprise RAG fixes this — when built right.
When it's built badly, it hallucinates with citations. We build it right — accuracy-measured, permission-aware, deployed in your cloud.
The result is a knowledge assistant your compliance team trusts and your teams actually use.
ConverseAI vs Glean vs DIY RAG
Honest positioning: Glean is excellent if you're a Fortune 500 comfortable with SaaS pricing and generic enterprise search. We fit when you want domain-tuned accuracy, your cloud, open models, or 4–6 week shipping.
| ConverseAI custom RAG | Glean / Guru | DIY (your engineering team) | |
|---|---|---|---|
| Deployment | Your cloud / VPC / on-prem | Glean SaaS, their cloud | Yours |
| Model choice | Any — Claude, GPT, Llama, Qwen | Glean's stack | You choose |
| Time to production | 4–6 weeks | 6-month rollout typical | 3–6+ months |
| Permission-aware retrieval | Yes (inherits source ACLs) | Yes | Depends on build quality |
| Citation-backed answers | Yes (configurable refuse-if-no-citation) | Yes | Depends |
| Custom domain accuracy | Tuned per engagement | Generic enterprise search | You tune |
| On-prem / air-gapped option | Yes | No (SaaS-only) | Yes |
| Best for | Teams wanting domain-tuned RAG with control | Large enterprises comfortable with SaaS + pricing | Teams with AI engineering talent + time |
What we build
Enterprise RAG and AI knowledge assistants deployed where your teams already work.
Employee knowledge assistants
Ask anything about policies, SOPs, benefits, IT, HR, compliance — get a cited answer in seconds. Deployed in Slack, Teams, your intranet, or a custom chat UI.
Customer support knowledge bots
Grounded on your help center, internal playbooks, and product docs. Deployable as a chatbot, WhatsApp bot, or agent-facing co-pilot.
Contract intelligence
Search, summarize, and extract clauses across thousands of agreements. Compare contracts. Flag missing terms. Auto-draft from templates.
SOP & compliance assistants
For regulated industries (BFSI, pharma, healthcare) — answers tied to the current version of a policy, with clause-level citations and audit trail.
Sales enablement assistants
Grounded on battle cards, win/loss notes, case studies. Instant answers during discovery calls.
RFP and proposal assistants
Pulls from your response library, past proposals, and product docs. Drafts responses with citations.
How we build it right
Grounded answers only, with citations.
Every answer links back to source documents and page numbers. If it can't cite, it won't answer — configurable per use case.
Permission-aware.
Users only see content they have access to in the source system (SharePoint ACLs, Google Drive permissions, Confluence groups). No data leakage across roles.
Your cloud, your control.
Deployed in your AWS/Azure/GCP VPC. Open models (Llama, Qwen, Mistral) for on-prem. No data leaves your perimeter.
Real-time sync.
When a document updates, the index updates. No stale answers from last quarter's policy.
Eval-first — the ConverseAI RAG Accuracy Benchmark.
We measure citation precision, answer accuracy, refusal correctness against a domain-specific test set built per engagement. Weekly eval review post-launch.
Multi-modal.
PDFs with tables and images, scanned documents, handwriting, audio transcripts — not just clean text.
Data sources we connect
We plug into your document stack, inherit permissions, and keep your index in sync.
How it works
Week 0 — Knowledge audit.
What you have, where it lives, access patterns, accuracy bar.
Weeks 1–3 — Build.
Ingestion pipeline, embeddings, retrieval, LLM orchestration, permissions, UI.
Week 4 — Deploy.
Internal pilot. Eval tuning.
Weeks 5–6 — Scale.
Full rollout, monitoring, feedback loop.
Why ConverseAI
- Built in your cloud, not ours. VPC/on-prem deployment with open models.
- Accuracy-measured. Every RAG build has an eval harness with citation precision + answer accuracy measured weekly.
- Conversational AI DNA. Ships as a real assistant — WhatsApp, Slack, Teams, web, voice — not just a search box.
- Domain-specific. Pharma SOPs, BFSI compliance, legal contracts — not generic enterprise search.
- India pricing, US quality. 4–6 week pilots vs. 6-month Glean rollouts.
Outcomes you can expect
- Answer policy/SOP questions in 5 seconds instead of 2 hours of searching
- Cut new-hire ramp time by 40–60%
- Find a specific contract clause across 10,000 agreements in under 10 seconds
- Deflect 30–50% of internal IT/HR tickets with a grounded assistant
- 100% citation-backed answers — zero hallucination tolerance for compliance
How we compare
Private knowledge AI vs generic LLMs vs document search.
| ConverseAI Knowledge AI | Generic LLM (ChatGPT, Copilot) | SharePoint / Confluence search | |
|---|---|---|---|
| Answers with citations | Yes — every answer sourced to document + page | No — hallucinated summaries | No — returns links, not answers |
| Handles proprietary docs | Yes — PDFs, Word, Confluence, SharePoint, Notion | Only if uploaded each session | Depends on indexing |
| Hallucination control | Strict — only answers from ingested corpus | Medium — may generate outside source | N/A — keyword match only |
| Private deployment | Yes — your cloud or on-premise | No — data leaves your environment | Yes (on-prem SharePoint) |
| Keeps up with new docs | Yes — automated re-ingestion pipeline | No — static training cutoff | Depends on crawl schedule |
| Best for | Teams that need accurate, cited answers from internal knowledge | General-purpose Q&A on public knowledge | Finding documents, not answering questions |
FAQs
How does RAG work on internal documents?
We ingest your documents, split them into chunks, create vector embeddings, and store them in a vector database. When you ask a question, the system retrieves the most relevant chunks and feeds them to an LLM to generate a grounded answer with citations.
Is enterprise RAG secure and private?
Yes, when built right. We deploy in your cloud, use permission-aware retrieval, and never train on your data. Open models for fully on-prem deployments.
RAG vs. fine-tuning — which is better for internal docs?
RAG for most cases. Handles frequent updates, provides citations, respects permissions. See the comparison table above.
How do you compare to Glean?
Glean is SaaS, priced per-seat, generic enterprise search. We build custom RAG in your cloud, with domain-tuning, open models, and lower year-1 cost at most scales. Honest: Glean is the right call for some Fortune 500 buyers; we're a better fit for teams wanting control, accuracy, or air-gapped deployment.
What accuracy can we expect?
Measured via our RAG Accuracy Benchmark per engagement. Typical deployments hit 85–95% answer accuracy with near-100% citation fidelity. We define the target accuracy bar with you in week 1.
How do you handle permissions — will employees see docs they shouldn't?
No. We inherit permissions from the source system (SharePoint ACLs, Google Drive permissions). The retrieval layer filters before content ever reaches the LLM.
Can it handle scanned PDFs, images, and tables?
Yes. Modern OCR, layout-aware parsing, and multi-modal embeddings handle complex documents including scans, diagrams, and tables.
What happens when documents are updated?
Real-time or scheduled re-indexing. Changes propagate in minutes.
Can we deploy on-prem, fully air-gapped?
Yes. Open models (Llama 3.3, Qwen, Mistral) on your hardware, no external API calls. Common for defense, BFSI, healthcare.