Generic chatbots guess. We build RAG chatbots grounded in your own documents, policies, and product information, so every answer comes from your content, with the source attached. When it doesn't know, it says so and hands the conversation to a human instead of inventing something.
Live in 3 weeks · Every answer traceable to a source
Trusted by teams in the US, UK, and India
Siddhraj
Unoloft
Kofeko
3nStar
Veda
Cerata
Shubham
Consultup IndiaSiddhrajUnoloftKofeko3nStarVedaCerataShubhamConsultup IndiaA standard chatbot answers from memory: whatever it absorbed during training, which never included your refund policy or your product catalogue. When it doesn't know, it produces something fluent and wrong, which is worse than saying nothing.
RAG, retrieval-augmented generation, changes the exam. Before the model answers anything, the system searches your own content, pulls the passages that actually relate to the question, and instructs the model to answer only from those. The answer comes back with the source attached, so anyone can check it.
That's the whole idea. Everything else on this page is engineering in service of one thing: making sure the right passage gets retrieved, every time.
The hard part isn't the model. It's the retrieval.
Same underlying architecture. What changes is whose questions it answers and what it's allowed to say.
Trained on your help docs, policies, product pages, and resolved tickets. Answers on your website, WhatsApp, and email, and escalates to your team with the full conversation attached the moment it's out of depth. Nobody has to repeat themselves to a human afterwards.
Best for: DTC brands and SaaS teams where the same twenty questions consume most of the inbox.
Your SOPs, HR policies, contracts, and process docs, made askable. Staff get an answer with a link to the source document in seconds instead of interrupting someone or digging through a shared drive nobody has organised since 2023.
Best for: Teams over 20 people where onboarding questions and “where is that document” are a daily tax.
Answers detailed pre-purchase questions from your specs, sizing charts, compatibility tables, and shipping rules. Handles the questions that make people abandon a cart, and passes qualified conversations to your sales team with context.
Best for: E-commerce catalogues with technical products, and B2B sites with long specification pages.
Point it at a contract set, a research library, a compliance manual, or years of reports, and ask questions in plain language. Every answer cites the document and section it came from, so it stands up to being checked.
Best for: Legal, accounting, consulting, and any team whose knowledge is locked inside PDFs.
We'll tell you on the first call. Sometimes the honest answer is better help docs, or a workflow automation at a tenth of the cost. We'd rather say that than sell you a build.
Book a discovery callWe get called in to fix other people's chatbots often enough to see the same four causes. Only one of them is about the model.
When a RAG chatbot gives a wrong answer, the model usually isn't the problem, it answered correctly from the wrong passage, because retrieval handed it the wrong passage. In our own project work, retrieval quality is consistently the thing that separates a chatbot that works from one that doesn't. Most builds skip straight to plugging in a model and never engineer the search.
Content gets chopped into fixed-size fragments, so a policy gets split down the middle and neither half makes sense on its own. Tables in PDFs turn to noise. The chatbot retrieves half an answer and confidently completes the rest.
It answered ten questions in the demo, so it shipped. There's no measure of how often it's right in production, which means nobody notices when it starts drifting, until a customer screenshots a wrong answer.
Prices changed, the returns policy was rewritten, three products were discontinued. The chatbot keeps answering from the version it was indexed on. Six months later it's confidently quoting a policy you no longer have. This is exactly why every build we ship comes with a monthly plan.
A demo needs a model and a prompt. Something you can put in front of customers needs six things.
We split your content on its real structure: sections, headings, clauses, table rows, so each retrievable piece makes sense on its own. Scanned PDFs get OCR, tables get handled as tables. Most retrieval failures are created here, before a single query is ever run.
We run semantic search alongside keyword search and fuse the results. Vector search alone misses exact terms, SKUs, product codes, policy numbers, names, which is precisely what customers type. Hybrid retrieval is the production default for good reason.
We retrieve a wide candidate set, then rescore it with a cross-encoder reranker so the strongest passages reach the model. It's the single highest-leverage precision improvement available in a RAG pipeline, and it's the step most builds skip because the demo works without it.
Every answer links back to the document and section it came from. Your team can audit any response in seconds, and customers can check it themselves. This is also what makes the chatbot defensible when someone disputes what it said.
We set a confidence threshold and instruct the system to escalate rather than answer below it. A chatbot that says “let me get a human” ten percent of the time is worth more than one that's confidently wrong two percent of the time.
Every answer is traceable to the passages and the query that produced it. When something goes wrong you can see exactly why in minutes instead of guessing at a prompt.
Every chatbot vendor promises accuracy. Ask what they measured and the conversation usually ends. Here's what we measure, before your chatbot talks to anyone.
Before development starts, we assemble a set of real questions, from your ticket history, your sales inbox, your team, with the correct answer and the document it should come from. This is agreed with you and it becomes the standard the build is held to.
Whether retrieval surfaced the right source. Whether the answer is actually supported by that source, with nothing added. Whether it answered the question that was asked. These fail independently, and lumping them into one “accuracy” number hides which part is broken.
Questions with no answer in your content, questions that span two documents, ambiguous phrasing, and questions it shouldn't answer at all. Refusing correctly is a pass, not a failure.
You get the scores and the failure cases in writing before the chatbot is live. If it's not good enough, we fix retrieval, that's what the fixed price covers.
The test set becomes a regression suite. Every time your content changes or we tune the system, we re-run it, so an improvement in one area doesn't quietly break another.
The fastest way to make customers hate a chatbot is to trap them in it. We design the exit before we design the conversation.
You choose what triggers a handoff: low confidence, a refund or billing question, a complaint, a named account, legal wording, or someone simply asking for a person. When it fires, the conversation moves to your team in Slack, your helpdesk, or email, with the full transcript, the customer's details, and what the chatbot already checked.
Every escalation gets logged as a coverage gap. Once a month we look at what got escalated, decide what should have been answerable, and add it to the knowledge base and the test set. Coverage goes up over time instead of sitting where it launched.
Under Article 50 of the EU AI Act, in force since 2 August 2026, anyone putting a chatbot in front of people in the EU must make clear they're interacting with an AI system unless it's already obvious. It applies to the business deploying the chatbot, not only to the model provider, and it reaches UK and US companies serving EU users. We build that disclosure into the interface by default rather than treating it as a legal afterthought.
Your documents are indexed for retrieval, not used to train a public model. We work with providers under business terms that exclude your data from model training, and we'll show you exactly which providers are in the pipeline before you sign anything.
The chatbot reaches only the content its job requires. You decide what's indexed, what's excluded, how long conversation logs are kept, and where they're stored, including EU or US hosting where residency matters.
Where policy requires it, we deploy inside your infrastructure or run open-weight models in a private environment. It costs more to run and we'll be honest about the accuracy trade-off before you choose it.
Most people arrive asking for one of these when they need another. Here's the honest version.
| You want it to | What you need | Why |
|---|---|---|
| Answer questions from your documents and policies | RAG chatbot | Your content changes constantly. RAG reads the current version at query time |
| Sound consistently like your brand, in a specific format | Fine-tuning, usually with RAG | Fine-tuning teaches style and structure, not facts |
| Actually do things: update the CRM, issue the refund, book the slot | An AI agent | A chatbot answers. An agent acts. Different build, different risk profile |
| Run a process end to end with nobody asking it anything | Workflow automation | If there's no conversation, you don't need a chatbot at all |
| Answer questions about one long document, occasionally | Probably nothing custom | Paste it into an off-the-shelf tool. We'll tell you when this is the answer |
Fine-tuning on your own documents to "teach it your business" is the most common expensive mistake we see. It's slow, it goes stale the day your content changes, and it doesn't give you sources.
One more distinction worth having: a chatbot answers a single question well. If your team wants something conversational that also drafts, summarises, and pulls context from your CRM or project tools while a person stays in charge of acting on it, that's an AI assistant, internal-facing and built for a different job than this page.
Want to know how the retrieval and model layer underneath a chatbot actually works? Generative AI & Custom LLMs is the engineering foundation this page is built on.
We look at your real questions, tickets, sales emails, whatever your team keeps getting asked, and your existing content. You find out here whether your documentation is good enough to ground a chatbot, and if it isn't, what the cheapest fix is. If the honest answer is that you don't need this, we'll say so.
A written scope, a fixed price, a delivery date, and the agreed test set, all before any code is written. If the scope moves, we re-quote in writing first.
We prepare your content, build the retrieval pipeline, and tune it against the test set. You get a written update every Friday and a short Loom walkthrough of what moved.
We score it against the full test set, including the questions it should refuse. You see the numbers and the failure cases before it meets a customer.
Deployed to your website, WhatsApp, helpdesk, or Slack, with the AI disclosure in place, escalation routing live, and logging from the first conversation.
Real questions reveal what the test set couldn't. We review escalations, close coverage gaps, re-index changed content, and re-run the regression suite. A named engineer stays on it.
Typical total: about 3 weeks from kickoff to live for a single chatbot on one content set. Multi-source or multi-language builds run 5–8 weeks.
The chatbot goes where your customers and staff already are, and reads from where your content already lives. Nobody should have to migrate a knowledge base to make this work.
The architecture barely changes by industry. What changes is the content it reads and what a human still has to sign off.
Client-facing chatbots built under your brand for your clients, or internal assistants over your own process docs and account knowledge.
Document Q&A over contracts, case files, and compliance manuals for consulting, legal, accounting, and recruiting firms.
Support and pre-purchase chatbots grounded in your policies, specs, and shipping rules, connected to Shopify and your helpdesk.
We agree a test set with you upfront and show you the scores before the chatbot meets a customer. Ask any other vendor what they measured.
Fixed scope, fixed price, in writing before work starts. We carry the risk of our own estimates.
A single production chatbot typically goes live in about three weeks. Enterprise vendors quote three months because they have three months of process.
No account manager relaying requirements to a team you never meet. You talk to the people building it.
Source code, prompts, the retrieval pipeline, the test set, and documentation transfer to you on final payment. No proprietary platform, no per-seat licence, no lock-in.
Every build includes a monthly plan: re-indexing, escalation review, coverage expansion, regression testing. It's scoped from day one, not sold to you at handover.
See what the monthly plan actually covers, month to month, cancel any time, and everything keeps running.
Book a 30-minute call. We'll look at your questions and your content, tell you honestly whether a chatbot is worth building, and give you a fixed price if it is.
