We design, build and run AI assistants on OpenAI, Claude, Gemini and open-source models, connected to your CRM, your documents and your channels. Your team asks in plain language, the assistant answers from your data and acts on it.
An AI assistant for business is a language model connected to your own systems and data, so it can answer questions and take actions inside your company instead of guessing from public information. The model supplies the reasoning. The value comes from what you connect it to.
A working business assistant has four parts:
Benefits
The assistant retrieves from your documents and records, and shows the source it used, so your team can check it.
Check an order, update a deal, book a slot, raise a ticket. The assistant finishes the task inside your systems.
Routine questions get answered at 2am the same way they do at 2pm, and anything unusual is handed to a person with the context attached.
We pick the model per task: cheap and fast for routine questions, frontier models for hard reasoning. OpenAI, Claude, Gemini, Mistral and open-source models such as Llama are all on the table.
Your assistant runs on your website, in your product or in your messaging platform. Nobody needs a ChatGPT subscription to use it.
EU data residency, access controls that respect who may see what, and human oversight on the decisions that need it. We are ISO 27001 and ISO 9001 certified.
The stack
Four layers, chosen per project. We are not tied to one vendor, which means the assistant can move when the market does.
Picked per task on cost, latency and reasoning quality, and swappable later.
Retrieval over your own content so answers carry a source instead of a guess.
Read and write in the systems you already run, through the platforms we partner on.
The assistant shows up where the work already happens.
Backed by industry-recognised certifications and performance metrics
500+
Clients
15000+
Automations
7+
Years of experience
Proof
Each one measured against a number the client already tracked before we started.
Legal · Knowledge
10-20 hrs/week saved
A retrieval assistant answers paralegal questions across tens of thousands of pages and cites the source document every time.
View case studyFinancial services · Sales
5-7 days to 5 minutes
A conversational assistant collects applicant details, verifies employment by API and writes the record straight into the CRM.
View case studyRetail · Support
30% faster
Foto Oskar handles customer orders and questions over WhatsApp with AI, cutting processing time without adding headcount.
View case studyUse-cases
Four shapes cover most briefs. Each one connects to different systems, which is what decides the build.
01 / 04
Where the goal is resolving tickets end to end rather than answering questions, we build a dedicated customer support AI agent.
Typical connections:
It recommends from your real catalogue, checks a live order, and changes it after the conversation instead of promising someone will follow up.
A private assistant inside Slack, Teams, Telegram or WhatsApp, connected to the tools your team already lives in.
Connected to HubSpot:
Connected to project tools such as ClickUp:
For companies sitting on contracts, case files, manuals or years of process documentation that nobody can search quickly.
This is the build behind our legal knowledge assistant, and it pairs well with contract and document automation.
Industries
The pattern is the same, the systems are not. These are the sectors we build for most often.
Order status, product recommendations and returns handled in chat, connected to your store and stock.
Ask questions across case files and contracts, and get an answer with the source document attached.
Conversational qualification and onboarding, with verification and CRM records written as the conversation runs.
Guest questions, bookings and billing queries answered from your property systems instead of a shared inbox.
In-product assistants and support deflection, connected through the APIs your platform already exposes.
Client-facing assistants you can resell, plus internal ones that take reporting and admin off the team.
How we build it
We pick the questions and tasks the assistant must handle, and agree the one number that says whether it worked. Vague briefs make assistants that demo well and get abandoned.
We map the documents, records and systems it needs, then build the retrieval and integrations so it reads and writes where the truth already lives.
We build the assistant, then test it against real questions from your business. Guardrails, escalation rules and access scoping are set here, not after launch.
Website, Slack, Teams, WhatsApp or in-product. Your team starts using it against real traffic while we watch how it behaves.
We review the conversations it got wrong, tune retrieval and prompts, and report against the number we set on day one.
Keep us on for monitoring, model updates and new integrations, or take ownership with the documentation and training to run it yourself.
A ChatGPT subscription is a good writing tool. It is not a business system. This is where the two part company. If you want an assistant that lives inside the ChatGPT interface instead, that is custom GPT development.
| Feature | ChatGPT out of the box | Your custom AI assistant |
|---|---|---|
| What it knows | Public information from its training data, plus whatever a person pastes into the chat | Your documents, records and live system data, retrieved on demand with the source attached |
| Taking action | Describes what should happen and leaves the doing to a person | Updates the CRM, checks an order, books the slot or raises the ticket inside your systems |
| Access control | Whatever the person using it can see and paste | Scoped per user and per role, so nobody retrieves what they are not allowed to see |
| Where it runs | Inside the ChatGPT interface, and everyone needs an account | Your website, product, Slack, Teams or WhatsApp, with no subscription needed to use it |
| Model choice | Whatever OpenAI ships, on OpenAI's timetable | Chosen per task across OpenAI, Claude, Gemini, Mistral and open-source models, and swappable later |
| Cost shape | A licence per seat, whether or not the seat uses it | A build cost, then usage that scales with the work it actually does |
Pricing
One use-case, one or two integrations, deployed on a single channel so you can prove the value before committing. Typically live in 3 to 4 weeks, quoted fixed-price after scoping.
Multiple use-cases, retrieval over your document library, several integrations and channels, with evaluation and guardrails. Typically 6 to 10 weeks, quoted fixed-price after scoping.
We monitor conversations, tune retrieval, handle model updates and add integrations as you grow. Billed monthly, scaled to usage and the number of systems connected.
A pilot assistant covering one use-case with a couple of integrations is the usual starting point, and a full production assistant with retrieval over your documents costs several times that. The three things that move the number are how many systems it has to read and write in, how much cleaning your data needs, and how many channels and languages it runs on. We quote fixed-price after a scoping call, so you are not signing up to an open-ended build.
ChatGPT answers from public training data. It cannot see this morning's orders, your pricing rules or your contracts, and it cannot update a record when the conversation ends. A custom assistant is connected to your systems, answers from your own content with the source attached, respects who is allowed to see what, and completes the task rather than describing it.
OpenAI removes the Assistants API on 26 August 2026, and calls to the assistants and threads endpoints stop working. There is no automated tool for moving your existing threads across, so anything built on it needs porting to the Responses and Conversations APIs, or to another provider. We do these migrations, and we usually take the opportunity to make the assistant model-independent so the next deprecation is a configuration change rather than a rebuild. If you are on it today, talk to us well before the date.
We pick per task rather than per vendor. Fast, low-cost models handle routine questions, and frontier models handle the reasoning that needs them. We work with OpenAI, Anthropic Claude, Google Gemini, Mistral and open-source models such as Llama, and we design the assistant so the model can be swapped without rewriting the integrations around it.
A pilot covering one use-case is usually live in 3 to 4 weeks. A production assistant with retrieval over your document library, several integrations and multiple channels typically runs 6 to 10 weeks. The variable is almost never the AI. It is how quickly we can get clean access to your data and systems.
Yes. We are a Make.com Platinum Partner and work daily with n8n, HubSpot, Airtable, Shopify and custom APIs, which is how the assistant reads live data and writes back into your systems. If a tool has an API, it can be connected. If it does not, we usually find another route into the same data.
We build for EU data residency, scope retrieval so people only reach what their role permits, and keep human oversight on the decisions that warrant it. Makeitfuture is ISO 27001 and ISO 9001 certified, and we design assistants to be reviewable, so you can show an auditor why the assistant answered the way it did.
No. It takes the repetitive questions and the lookups, so your people spend their time on the work that needs judgement. In practice the assistant handles the first line and hands anything unusual to a person with the full context attached, which is faster for the customer and less tedious for the team.
Let's find together where you can implement AI and automation securely, and built to last.
