Chatbots and copilots
that know your business
A general-purpose model knows everything and nothing about you. We build the layer that gives it your content, your rules and your tone, then prove it behaves before anyone talks to it.
assistant on your content
in front of users
against an evaluation set
when a provider is down
What we build
Six shapes cover most of what teams ask for. All of them ship inside your product, not as a separate tool.
Customer-facing chat
Answers on your product, pricing and policies, with escalation to a person the moment the conversation needs one.
Internal copilots
A colleague that has read every document your team has, available in the tool where the work already happens.
Drafting and summarising
First drafts of replies, reports and summaries in your own voice, with the source material attached for review.
Structured extraction
Free text turned into the fields your database expects, validated against a schema so nothing malformed gets written.
Multi-turn workflows
Conversations that gather what they need across several turns, call your APIs, and confirm before acting.
Voice assistants
The same assistant over the phone or in-app audio, with transcripts written back to the record.
How it works
The assistant never invents policy. It answers from what you gave it, and says so when it does not know.
What it learns from
- Product docs and help centre
- Policies, pricing, contracts
- Ticket and chat history
- Your database, read-only
The application layer
- Retrieval over your content
- Prompt and version control
- Output schemas and guardrails
- Evaluation set on every change
- Escalation to a human
Delivery
- Web, in-app, Slack or phone
- Frontier or self-hosted models
- Session memory and analytics
- Cost and latency budgets
How we work
Each step ends in something you can try yourself.
Gather the ground truth
A week collecting the documents, policies and past conversations the assistant will answer from, and agreeing what it must never say.
Prototype
A working assistant on your real content in two to three weeks. You talk to it, break it, and tell us where it is wrong.
Harden
Guardrails, output schemas and escalation rules. This is where a demo becomes something you can put in front of a customer.
Evaluate
A scored set of real questions with expected answers. Every prompt or model change runs against it before release.
Operate
Conversation quality, deflection rate, cost per conversation and latency on one dashboard, reviewed with you monthly.
Built by a team that ships
The AI layer is new. The engineering underneath it is not. These are products we designed, built and still maintain.
Data collection at scale
A digital market study and data-collection platform built for fast, reliable research at scale.
Product development · Frontend
Digital marketing
One content workflow
Planable bundles social media collaboration tools into one seamless content workflow for marketing teams.
Web development · Performance
Social platform
Built for real connection
OPEN social merges the benefits of social networking with real, meaningful human connection.
Mobile · ProductBuilt to be changed
Assistants age badly when the stack is welded shut. Every layer here can be swapped without touching the rest.
Claude, GPT, Gemini or an open model
Chosen per task. A strong model for reasoning, a small fast one for classification and routing, so quality and cost are tuned separately.
An in-app widget, Slack, Teams or the phone
The same assistant behind several front ends. Adding a channel is configuration, not a second build.
Per session, per user, or none at all
Chosen with your privacy policy in hand. Plenty of assistants work better with no memory at all, and that is a lower-risk default.
Output schemas, topic limits and a way to escalate
Constraining what the model may return matters more than clever prompting. Anything outside the schema never reaches the user.
The provider API, your own VPC, or fully on-prem
Set by your data policy. If conversations cannot leave your infrastructure we run an open model inside it.
“Working with them felt smooth and efficient, and we always felt heard and supported.”
Ekart Dragos-Ioan · CEO, SIGMA LIFE IT Verified review on ClutchGot an idea? Let’s make it real.
Tell us the short version
This could be the first step towards a new and successful collaboration. A one-line idea and a finished spec are both fine — tell us the problem, the deadline you’re working to and what’s in your way.
Frequently asked questions
Only if that is what the job needs. Most of what we build is narrower and more useful than a general chatbot: an assistant that answers on one domain, drafts one kind of document, or handles one part of a conversation and hands the rest to a person.
By constraining what it can say rather than hoping the prompt holds. Answers are grounded in your own content, the output shape is fixed by a schema, topics outside scope are refused, and an evaluation set runs on every change before release.
Yes, and that is usually where we start. Your documents, policies and past conversations become the material the assistant answers from, kept in sync so it reflects what is true this week.
It says so and escalates. An assistant that admits the gap and hands over cleanly is worth far more than one that produces a confident paragraph nobody can rely on.
Web and in-app widgets, Slack, Teams and phone. The assistant is built once behind an API, so adding a channel is configuration rather than a second project.
A working version on your own content in two to three weeks, and a first release in front of users usually inside six to eight weeks once guardrails and evaluations are in place.