Customer facing assistants
A chat widget on your site or app that answers product, pricing, and policy questions from approved sources, captures leads, and hands off to your team with the full conversation attached.
Chatbots and RAG
Chat that answers from your data,
with receipts
Retrieval augmented chat assistants that read your documents, your product catalogue, your policies, and your database, then answer with citations you can click. Accurate on day one, honest when it does not know.
RAGVector SearchCitationsMulti Tenant
80%
Of routine questions deflected
Instant
Answers pulled from live sources
Every claim
Traced back to a source document
Why It Matters
A generic chatbot guesses. A grounded one looks it up.
The reason most chatbot projects quietly die is simple. A model trained on the public internet knows nothing about your refund policy, your enterprise pricing, your onboarding steps, or the contract you signed in March. When it does not know, it improvises, and one confident invention is enough to destroy trust in the whole thing.
Retrieval augmented generation fixes that at the root. Before the model writes a word, we search your own content for the passages that actually answer the question, hand those passages to the model, and require it to answer from them. What comes back is grounded, current, and footnoted with links to the source.
We treat retrieval quality as the real engineering problem, because it is. Clean parsing of messy PDFs, sensible chunking, hybrid keyword and semantic search, reranking, and permission filters so a user only ever sees what they are allowed to see. The chat interface is the easy part.
How we keep it honest
- Answers cite the exact document, page, or record they came from
- Says it does not know instead of inventing, and offers a human handoff
- Respects your permissions so each user sees only their own data
- Refreshes automatically as your documents and records change
Capabilities
What we build
From a support widget on your marketing site to a private assistant across an entire document estate.
Internal knowledge assistants
One place your team can ask anything about policies, processes, contracts, and past projects, with answers drawn from the documents that are actually current.
Document intelligence
Parsing and structuring of contracts, reports, invoices, and scanned files, including tables and images, so the messy material in your drive becomes queryable.
Hybrid retrieval pipelines
Semantic search combined with keyword matching and a reranking pass, tuned per data set, because pure vector search alone quietly misses the exact answers people need.
Multi tenant SaaS chat
Chat built into your own product, with strict tenant isolation, per customer knowledge bases, usage metering, and branding controls your customers can manage.
Accuracy and evaluation
A test set of real questions with approved answers, scored on every change, plus feedback capture in the interface so the assistant measurably improves over time.
How We Work
From first conversation to production
No six month discovery phase. We move in short cycles and you see working software early.
01
Source audit
We inventory what you have, from clean web content to a decade of PDFs, decide what belongs in the knowledge base, and flag what is out of date before it poisons the answers.
02
Ingestion and indexing
Documents are parsed, cleaned, chunked with structure preserved, embedded, and indexed, with a refresh pipeline so new and changed content flows in automatically.
03
Retrieval tuning
We test real questions against the index, tune chunking, hybrid search weights, and reranking, and keep going until the right passage lands in the top results consistently.
04
Interface and guardrails
We build the chat surface, the citation display, the fallback and escalation behaviour, and the permission filters, then wire it into your site, app, or internal tools.
05
Launch and improve
We go live, watch the questions your users actually ask, close the gaps in the knowledge base, and report on deflection, satisfaction, and accuracy every month.
Tools we build with
OpenAIClaudeLangChainPineconepgvectorQdrantCohere RerankPythonFastAPINext.jsPostgreSQLAWS
Let's Talk
Send us your ten hardest questions. We will answer them from your own documents.
Give us a sample of your content and the questions your customers or staff keep asking. We will come back with a working retrieval demo and an honest read on accuracy.
We reply within 24 hours with a clear plan, a timeline, and a number.
Use Cases
Where grounded chat changes the numbers
Patterns we have shipped before, so we already know where the traps are.
Support deflection
The repetitive eighty percent of tickets answered instantly and correctly, so your support team spends its day on the cases that genuinely need a person.
Sales enablement
Reps ask about pricing rules, competitor positioning, and past proposals in plain language and get the current answer with the source attached, in seconds.
Employee onboarding
New hires stop interrupting senior staff for policy and process questions and get consistent answers from the handbook, the wiki, and the systems documentation.
Contract and policy review
Ask what a clause means across hundreds of agreements, find the exceptions, and jump straight to the page that proves it rather than reading everything.
Product and catalogue search
Shoppers describe what they want in their own words and get real recommendations from live inventory, pricing, and specifications rather than keyword noise.
Chat inside your SaaS
An assistant embedded in your product that answers from each customer's own workspace data, isolated per tenant and metered per plan.
What You Get
Everything handed over, nothing held hostage
You finish the engagement owning the system, the code, and the knowledge to run it without us. That is the point.
- A deployed chat assistant on your site, app, or internal tooling
- An ingestion pipeline that keeps the knowledge base current without manual work
- Retrieval configuration tuned and benchmarked against your real questions
- Citations, fallbacks, and human escalation built into the interface
- Analytics on deflection rate, unanswered questions, and user feedback
- Thirty days of accuracy tuning after launch
FAQ
Chatbots and RAG, answered straight
The questions every serious buyer asks us before the first invoice.
How do you stop it from making things up?
The model only answers from retrieved passages, and we require citations for every claim. When retrieval finds nothing relevant, the assistant says so and offers a handoff instead of guessing. We measure this with an evaluation set before launch.
What formats can it read?
PDFs including scans, Word, Excel, PowerPoint, HTML, Markdown, Notion, Confluence, Google Drive, help desk articles, and direct database or API sources. Tables and images inside documents are handled too.
How does it stay up to date?
We connect the assistant to your sources rather than uploading a snapshot. New and edited content is reindexed on a schedule or on a webhook, so answers reflect the current version rather than last quarter's.
Can different users see different answers?
Yes. Permissions are enforced at retrieval time, so a user only ever gets passages from documents they are allowed to read. This is essential for internal assistants and multi tenant products.
Where does our data live?
Wherever you need it to. Your own cloud account, a managed vector database, or fully self hosted with open weight models when the data cannot leave your walls. Your content is never used to train public models.
How long does a build take?
A focused assistant over a clean set of documents can launch in two to three weeks. Large, messy document estates with strict permissions typically take six to eight.
Keep Exploring
Explore the rest of the stack
Most of our work combines two or three of these. Start where the pain is loudest.
Ready to Start?
Let's build the version of this
that actually ships
Tell us the problem in plain language. We will tell you whether it is worth building, what it takes, and what it costs, before you spend a rupee or a dollar.
We reply within 24 hours with a clear plan, a timeline, and a number.