Professional Services · Knowledge management AI
Your firm's expertise is in documents nobody reads twice
Banao builds knowledge retrieval systems that let any fee-earner query a decade of past matters in plain language and get an answer with the source citation — not a folder listing, not a keyword search, not a request to the partner who wrote it.
The system indexes your DMS, precedent bank, and internal memos. Every answer traces to the original file page. Juniors stop rebuilding memos that already exist. Partners stop being interrupted for institutional memory.
A 200-partner advisory firm— past engagement files made queryable, answers traced to source documents.
The first call is free · 45 minutes · no obligation
What we build
What a Banao knowledge retrieval deployment includes
The gap between a firm's institutional knowledge and the work being done today is where hours leak. These are the components that close it.
Plain-language query over your own matter history
Fee-earners ask a question in plain language — 'what did we argue on force majeure in 2021?' — and get a cited answer from your own files, not a general-web result or a folder of documents to read.
Source-traced citations, not summaries
Every answer links to the specific file and page it came from, so a fee-earner can verify the answer before acting on it. The system is a research starting point, not a black box.
Secure indexing inside your DMS
The index lives inside your document management system, behind your existing access controls. Matter data does not leave the boundary you define, and access is scoped by role and matter team.
Precedent and clause retrieval
Find prior clauses, argument constructions, and precedent passages across your own transactional and advisory work — without knowing which file or year to look in.
Expert-location across the firm
Identify which fee-earner last worked on a topic, sector, or jurisdiction — based on actual matter history, not what people remember to put in a directory.
Continuous index updates
New matters and memos are indexed as they are filed. There is no manual curation step and no stale knowledge base — the index reflects what the firm knows today.
Receipts
Where this is already running
Metrics shown dotted (··) are being finalised in our case-study metrics pack. The deployments are live; numbers will be published once verified. Client names are withheld where confidentiality terms require it.
A decade of engagement files made queryable in plain language
Ten years of memos, working papers, and past engagement outputs sat in a DMS that staff could only browse by folder. Banao built a retrieval layer over the firm's own documents — staff now ask a question in plain English and receive an answer cited to the source file, without knowing which engagement it came from.
Clause and precedent retrieval across eight years of deal files
Associates regularly re-drafted clauses that already existed in prior deal files. Banao indexed the firm's transaction archive and wired it to a plain-language query interface — associates retrieve the right clause from the right deal in minutes rather than requesting it from the partner who negotiated it.
Dogfooding
We run institutional knowledge on the same tools
Banao is a ~300-person firm. We carry seven years of past engineering engagements, internal playbooks, and technical memos. Our own knowledge retrieval runs on the same stack we deploy for clients — before we quote a system, we live on it.
InterviewGod screens our own hires; Vikaas runs our own demand generation. A tool that survives inside a busy delivery operation has already proved something a vendor demo cannot.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When knowledge AI is the wrong investment
Not every firm has a knowledge problem an AI system will fix. We will tell you before the build starts:
- Unstructured or unfiled work: if past matters are poorly filed or exist outside your DMS, week one is data organisation, not AI. We scope that honestly in the Discovery Sprint.
- Low reuse frequency: if your practice areas are narrow and your team holds the relevant knowledge in their heads, the retrieval layer will not earn back its maintenance cost.
- Confidentiality constraints that preclude indexing: some matter types cannot be indexed even within your own environment under engagement terms. We identify these in discovery before touching a document.
How we start
How we start — prove it on your own files first
We don't propose a knowledge platform before we've seen what your documents contain and how your people currently search.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit a sample of your actual matter files, test retrieval accuracy on questions your fee-earners actually ask, and hand back a baseline performance estimate with a go/no-go — yours to keep. If you proceed, the Sprint is credited against the build.
- 02
Build
Secure indexing integrated with your DMS and access controls. We tune retrieval on your own documents and precedent conventions, not a generic legal corpus.
- 03
Production & adoption
Roll-out with citation verification built in, plus the change management fee-earners need to trust AI answers. The index updates continuously as new matters are filed.
FAQ
Frequently asked questions
Can the system respect matter confidentiality walls?
Yes. Access controls from your DMS carry through to the retrieval layer — a fee-earner only receives answers from matters they are cleared to see. Matter team scoping and conflict walls are part of the deployment architecture discussion in week one.
What document types can it index?
Word documents, PDFs, email threads, and standard DMS formats. If your files live in iManage, NetDocuments, SharePoint, or a similar system, the index connects directly. Scanned pages require OCR as a pre-processing step, which we factor into the build scope.
How do you stop the system from generating an answer that is not in the files?
Every answer is grounded in retrieved passages from your actual documents, not generated from a general training corpus. If a source passage cannot be located to support the answer, the system says so. Citation links are mandatory on every response, so a fee-earner can check the original before acting on it.
How long before fee-earners can query the system?
Two weeks for the Discovery Sprint, then a 6–8 week build and a 3–4 week rollout with adoption support. Initial indexing of your existing matter archive runs during the build, so the knowledge base is populated before go-live.
Will fee-earners actually use it?
That depends on whether it gives better answers faster than asking a colleague. We pilot with a small group during rollout, measure query volume and answer usefulness, and adjust retrieval before full firm deployment. Adoption is a deliverable, not an assumption.
Get started
Find out what is buried in your matter archive
Bring the question your fee-earners cannot currently answer without calling a partner. In 45 minutes we will show you whether retrieval AI can answer it from your own files.
Book a Discovery Sprint