Your answers already exist — buried across contracts, reports, research, and intranets nobody has time to read. Most teams point an LLM at those documents and watch it hallucinate, cite the wrong source, or surface data a user shouldn't see. Banao builds research agents on retrieval-augmented generation: every answer is traced to a source passage and scoped to the asking user's access. It's the same grounding stack we run across our own 300-engineer operation since 2017.
The gap isn't a smarter chatbot — it's the retrieval layer underneath it. Banao indexes your structured and unstructured knowledge (documents, research, support tickets, internal wikis), grounds every response in retrieved passages, and returns cited, permission-aware answers instead of confident guesses. We ship a working pilot in 3–5 weeks, evaluate it against your real questions, then harden it for production. These same grounding patterns run inside Banao first: our internal Vidya knowledge system answers our 300-person team across India, UAE, UK, and US from the same architecture before any of it reaches a client.
Retrieval-grounded agents that answer from your documents, cite their sources, and respect who's allowed to see what.
Book a 45-min scoping call
Answers grounded in your own documents
Retrieval-augmented agents that pull from your contracts, reports, and wikis and cite the exact passage behind every answer — so reviewers verify, not just trust.
Semantic search that finds meaning, not keywords
Vector-indexed search so a plain-English question surfaces the right clause, study, or ticket even when the wording doesn't match.
Market intelligence delivered as a sourced brief
Agents monitor competitor moves, regulatory changes, and industry news, then hand your team a cited summary instead of a raw link dump.
Literature review and synthesis, compressed
Automated synthesis and citation gathering for R&D, academia, and legal teams — weeks of reading become a referenced summary experts can audit.
Reports your stakeholders actually read
Generated briefs, dashboards, and visual summaries grounded in retrieved evidence, so every chart traces back to a source.
Multilingual, multimodal knowledge agents
Agents that read, summarize, and translate text, audio, and video across languages — built for GCC and global teams working across jurisdictions.
Agents embedded where your team already works
Knowledge agents wired into your intranet, enterprise search, Slack, or workflow tools — answers arrive in context, not in another tab.
Domain-tuned agents for regulated work
Bespoke agents for legal, scientific, or financial domains — evaluated against your own question set and tuned to your taxonomy before they go live.
Where research agents change the operation
Retail & E-commerce
Agents monitor competitor launches, pricing shifts, and review sentiment, then deliver a sourced daily brief — so merchandising teams decide on current signal, not last quarter's report.
EdTech & Academia
Automated literature review and synthesis turn weeks of reading into referenced summaries, with an audit trail back to every cited paper.
Healthcare & Life Sciences
Agents extract findings from journals, trial reports, and clinical notes with citations intact — accelerating evidence review while keeping every claim traceable for compliance.
Banking & Finance
Agents condense filings, regulatory updates, and analyst reports into risk and opportunity briefs, surfacing the paragraph that matters instead of a 90-page PDF.
Manufacturing & R&D
Patent searches, technical literature, and innovation-trend analysis run as scheduled agents, shortening the research cycle ahead of product and process decisions.
Legal & Compliance
Agents surface relevant case law, flag clauses across contract sets, and ground every answer in the source document — so counsel reviews a citation, not a guess.
Recent Work
Majra, the UAE's national CSR and sustainability authority, was losing staff hours to bilingual content scattered across platforms, with no unified way to find internal knowledge. Banao built an English/Arabic AI chatbot grounded in Majra's own documents and wired it into their SharePoint intranet and a role-based e-learning hub. Hunting for documents across systems became a single conversation, and internal knowledge and learning engagement moved onto one adopted system instead of fragmented tools.
NLP & Retrieval | Workflow Automation | Agentic AI
Automated Research
A university research group was losing weeks to manual literature review, with analysts re-reading the same papers and missing connections across them. Banao built a retrieval-augmented synthesis platform that grounds every summary in its source paper and exposes the full citation trail, so reviewers verify each claim instead of trusting the model. Review time fell by roughly 70%, and researchers began surfacing cross-study insights the manual process had missed.
We assess research goals, data sources, compliance needs, and reporting requirements to define agent capabilities, AI workflows, and measurable KPIs for knowledge automation. Why this matters: most knowledge-agent projects fail because no one defined which questions the agent must answer well — we lock that evaluation set first, so success is measurable, not anecdotal.
Gather structured and unstructured data including documents, academic papers, news, and enterprise records—then integrate with knowledge bases, APIs, and data lakes to build a strong foundation for agent training. Why this matters: ungoverned ingestion is how agents end up surfacing documents a user shouldn't see — we map access permissions into the index from day one, not as an afterthought.
Select and fine-tune AI models for summarization, information extraction, semantic search, and trend analysis—training them on domain-specific data to deliver accurate and context-aware insights. Why this matters: the retrieval layer, not the model, decides answer quality — we tune chunking, embeddings, and ranking against your data instead of trusting an off-the-shelf default.
Conduct rigorous testing for summarization accuracy, contextual relevance, bias detection, and compliance—ensuring the AI research agents perform reliably in real-world knowledge discovery scenarios. Why this matters: a demo that works on ten questions can break on the eleventh — we evaluate grounding, citation accuracy, and hallucination rate against your real query set before launch.
Deploy agents on cloud, on-premises, or embedded within enterprise applications—seamlessly integrating with dashboards, reporting tools, and workflow automation systems. Why this matters: an agent nobody opens delivers nothing — we embed it where work already happens, so adoption doesn't depend on changing your team's habits.
Continuously monitor agent usage, retrain models with new data, and expand features to adapt to evolving research needs—ensuring long-term accuracy, scalability, and business value. Why this matters: knowledge drifts as documents change — we retrain on new content and monitor answer quality so accuracy holds months after launch, not just at handoff.
Client Voices: Research AI Results
Head of Research
Research & academia
Head of Market Intelligence
Financial services
Grounded summaries we can audit
The agent cut our literature-review cycle and cites the source passage behind every claim, so researchers verify findings instead of trusting the model. That citation trail is what cleared it for peer-reviewed work — and what our last document-AI attempt never had.
Here is what you will get for submitting your contact details.
45 minutes of free consultationA strict non-disclosure agreementFree market & competitive analysisSuggestions on revenue models & planningDetailed feature list documentNo obligation proposalAction plan to kick start your projectWe tried 'ChatGPT on our documents' and it hallucinated — how is this different?
That failure is almost always the retrieval layer, not the model. We use retrieval-augmented generation: the agent answers only from passages it retrieves from your documents and cites each one. We evaluate grounding and hallucination rate against your real questions before launch, and tune chunking and ranking until answers hold up.
How do you stop the agent from making up answers or citing the wrong source?
Every answer is grounded in retrieved source passages and returned with citations; when the knowledge base has no good match, the agent is built to say so rather than guess. We treat citation accuracy and hallucination rate as explicit QA gates and tune retrieval until they meet your threshold.
Can it respect who's allowed to see what?
Yes. We map your access permissions into the retrieval index, so the agent only surfaces documents the asking user is cleared to see. It's built in from ingestion, not bolted on — critical for legal, HR, finance, and regulated data.
Who owns our data, the index, and the models?
You do — 100%. Your documents, the vector index, custom code, and any fine-tuned models are yours. We sign a mutual NDA before detailed discussions and DPAs for regulated industries, and we don't retain your data or build derivative products on it.
Should we build this in-house or partner with you?
In-house RAG teams typically take 12–18 months because retrieval evaluation, grounding, and access control are harder than the demo suggests. This is our day job — we compress it to a 3–5 week pilot, and we run the same patterns inside Banao first, so you inherit the scar tissue instead of paying for it.
Will it integrate with our intranet, search, and existing tools?
Yes — we're stack-agnostic and embed agents into your intranet, enterprise search, Slack, or workflow tools, and connect to knowledge bases, document stores, and APIs. Answers arrive in the systems your team already uses.
What does a knowledge agent cost, and how soon is it live?
A production-grade build typically runs $50K–$250K depending on data volume, integrations, and compliance scope, with a working pilot in 3–5 weeks and full deployment in 2–3 months. We lock the exact number after a short scoping conversation — book a 45-minute scoping call and we'll map it.
