AI agents
AI agents that carry out tasks on their own, inside your systems
Not a chatbot that only talks, but an agent that calls real functions, is grounded on your data, and runs with guardrails inside your existing tools. You keep the judgement, the agent does the work.
- Grounded on your own data, with source citations
- Runs inside your real systems, not a side tab
- Guardrails and human-in-the-loop
In short
Building an AI agent means going beyond a chatbot that only talks. An agent carries out tasks on its own: it calls real functions, searches your data, and produces actual work, not loose text. We build agents that run inside your existing systems, are grounded on your own sources with source citations, and that have guardrails plus a human-in-the-loop so you keep control. At Simply a native agentic AI chat drives platform functions from a single prompt; at Planit, Claude finds precedents in seconds with a citation back to the original document. Fixed price agreed upfront, 100% ownership of the full code.
What gets in the way
When most people think of AI, they think of a chatbot: you type something, you get a bit of text back. Nice, but it changes nothing about your actual work. An agent is different. It carries out tasks on its own: it calls functions, searches your data, fills in a document, starts a flow. Not talking about the work, doing it. The difference comes down to three things we build in as standard. One: the agent calls real functions in your system, not loose answers. At Simply a native agentic AI chat drives the platform functions and automations from a single prompt. Two: the agent is grounded on your own sources, with a citation back to the document, so it doesn't just make things up. At Planit Consulting, Claude runs as a RAG layer over 18,429 documents and gives a source citation with every answer. Three: there are guardrails on it and a human can step in where it matters. That's how you win time without losing control. And we're honest about when a simple automation or a plain chatbot fits better than an agent. We think along with you: we listen first to what you want to achieve now and where you are heading, then choose the technology to match, for the most scalable and powerful result that is cost-efficient at the same time.
Chatbots that only talk but do nothing
A chatbot answers questions, and that's where it stops. It can't create a document, start a flow, or update a record. You get a bit of text and still have to do the work yourself. At Simply the agentic AI chat works the other way around: one prompt drives platform functions and automations, and the work actually happens.
Agents that hallucinate without grounding
An agent without grounding confidently invents things that aren't true. For legal, financial or client work that's useless. At Planit Consulting we solved it with a RAG layer: Claude answers only from the archive and gives a citation back to the original document, so consultants can always verify.
Agents stuck in a separate tab
An AI tool sitting next to your real system means you copy data back and forth. That instantly burns the time you were supposed to save. At Simply the whole AI stack runs native in Salesforce, in Apex, not in an iframe alongside it. The agent sits where the work already happens.
No guardrails, no control
An agent that acts on its own with no limits or checkpoint is a risk. What is it allowed to touch, and where does a human still need to look? Without those agreements you never know for sure the outcome is right. We build guardrails and a human-in-the-loop in from the very first setup.
What we build
How we help
Agentic chat that calls real functions
Not a loose answer, but an agent that acts. We build a native, integrated agentic AI chat that drives functions and other automations in your system from a single prompt. The user says what needs to happen, the agent carries it out inside the tools your team already uses.
RAG-grounded agents with source citations
An agent that answers only from your own sources, not from what the model thinks it knows. The agent runs as a RAG layer over a vector store of your documents, and gives a citation back to the original document with every answer. Verifiable, so usable.
Agents that produce work, not just text
The difference between talking and doing. Think of an agentic code engine: a large library of examples is vectorised, the agent searches it and uses them as a guide, and fills in a project template. The result is working, scalable code, not a block of text in one giant file.
Grounding on your own data via embeddings
An agent is only useful once it knows your documents, conversations and records. We turn your sources into embeddings and a vector store so the agent can search them. From vectorising individual summaries for later search to embeddings over an entire archive.
Guardrails and human-in-the-loop
We define what the agent may and may not touch, and build in a checkpoint where human judgement matters. The predictable steps run automatically, the sensitive decisions stay with your people. That's how the agent acts on its own without you losing your grip.
Proof
We have built this
Real projects we can name.
Simply
Recruitment SaaS
An AI recruitment suite, a native Salesforce app, a secure back-office and a marketing site. The full Simply stack, built end to end.
4-6 hrs/week
Recruiter time saved
Native Salesforce (Apex)
Integration
Planit Consulting
Planning consultancy
An AI chat that makes 18,429 planning documents searchable, with source citations.
18,429
Documents
95,592
Chunks
GameChef
Game development / AI SaaS
A browser-based, AI-powered game engine with a visual editor and code, art and sound tooling.
Days, not weeks
Build time
Code · Art · Sound
Channels
Our approach
From idea to working software in four phases
Every project runs through the same four phases, so you always know what's happening, what comes next and what it costs — from first call to live software, usually in weeks rather than months.
- 01 Phase 1 Free intake
Understand & analyse
Everything starts with a good conversation. We map your goals, processes and the bottlenecks worth solving — no sales pitch, just an honest read on where the biggest win sits.
- 02 Phase 2 Free blueprint
Blueprint & quote
We turn the analysis into a blueprint: a clear plan showing exactly which steps we'll take and why, paired with one fixed quote. You know up front precisely what we build, what it costs and when it's done — no open ends, no hourly billing, no surprises afterwards. And getting that blueprint is completely free, no obligation.
- 03 Phase 3 Prototype in days
Build, test & deploy
Then we build, in short iterations. You see a working prototype in days and a finished product in weeks. We work on a modern, AI-native and secure stack — with data protection and GDPR in mind from day one.
- 04 Phase 4 100% your code
Implement & optimise
We launch, hand over the full codebase and keep improving on your terms. You get 100% ownership of the code we write — no lock-in, no licensing games. Hosting and maintenance are optional, never required.
What you always get
Fixed price
Agreed up front, never an open end.
100% code ownership
Your code, fully yours, no lock-in.
Live in weeks
Prototypes in days, finished in weeks.
Modern & secure
A scalable stack that always integrates with the latest tech. Secured by our in-house cybersecurity experts.
Tools & tech
We're not tied to one model or platform. We pick the AI approach that fits your data, budget and goal — an off-the-shelf model, a custom pipeline, or a mix — and we're honest when simpler tech does the job just as well. No hype, no lock-in.
What you get
- An AI agent that carries out tasks on its own inside your systems
- Function calling: the agent invokes real functions and automations, not loose text
- Grounding on your own data through embeddings and a vector store
- RAG with citations back to the original document, so every answer is verifiable
- Guardrails that define what the agent may and may not touch
- A human-in-the-loop checkpoint where human judgement matters
- The full code and flows fully yours, with documentation and handover
- A fixed price agreed upfront, with no open end
Our promise
Your software stays yours
- Full code ownership The full source code is and stays yours, documented and ready to hand over.
- No vendor lock-in Stop working with us and your system keeps running. Any developer can pick it up.
- Fixed price up front You get a no-obligation blueprint and quote up front: no open-ended billing and no surprises afterwards.
- Security in every layer We build security-by-design, with a specialist in-house.
Frequently asked questions
What does it cost to have an AI agent built?
It depends on what the agent has to do, but you always get a fixed price agreed upfront. An agent that calls a few functions in one system is a matter of days to weeks. An agent grounded on a whole archive, like the RAG layer for Planit over 18,429 documents, costs more because there's a full pipeline underneath: classification, chunking, embeddings, a vector store. We map what you genuinely need first, then quote. That way you know where you stand from the start.
How long until an agent goes live?
A simple agentic chat that drives a few functions often goes live within days to weeks. An agent that has to be grounded on your own data takes longer, because the quality of the answers stands or falls with the pipeline underneath: how you classify, chunk and embed your documents. We work in short iterations, so you see a working version quickly. The exact timeline depends on how many sources and functions are involved, and we agree that upfront.
Does the agent stay mine, and who maintains it?
The agent runs on your own environment and the code, prompts and flows are fully yours, with documentation and a clean handover. No vendor lock-in: your own team or another party can carry on with it. If you want us to maintain and develop it further, for example when your sources change, we can, with clear agreements, but you are never tied to it.
Will an AI agent replace my people?
No, it takes the repetitive work off their plate. The principle is simple: people do the judgement work, the agent does the execution. At Planit the consultant still decides what's relevant; the agent finds the precedents in seconds and supplies the source. At Simply, recruiters still run the conversations; the agentic chat takes over the admin around them. That's exactly why we always build in a human-in-the-loop: the sensitive decisions stay with your team.
How do you stop the agent from making things up?
With grounding and guardrails. We don't let the agent answer from what the model thinks it knows, but from your own sources, through a RAG layer with embeddings and a vector store. At Planit, Claude answers only from the archive and gives a citation back to the original document with every answer, so you can verify it. On top of that we define what the agent may touch and build in a checkpoint where it's needed.
Do you really need an agent here, or will a chatbot do?
Honest answer: often an agent is overkill. If all you need is to answer a question from a fixed set of texts, a plain chatbot or a simple search is cheaper and more stable. An agent only pays off when something actually has to happen: calling a function, building a document, starting a flow, searching a large archive with citations. We'll just say so if a lighter solution will do, instead of bolting AI on because it sounds modern.
Ready to build?
Book a no-obligation call — we'd love to think through the best approach with you.
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