One engineer, start to finish
The person who scopes your project is the person who builds it. No account managers, no handoffs, no telephone game between sales and delivery.
AI that works on your data, not just in a demo.
Retrieval-augmented assistants, task agents and private LLMs, wired into the tools your team already uses, with evaluation before anything goes live.
From $2,000Why it matters
The case for doing this properly.
An AI system is a language model connected to your own data and tools so it can answer or act on real business context. That usually means RAG (retrieval-augmented generation) over your documents, agents that can take actions through APIs with guardrails, or a private LLM that keeps sensitive data on infrastructure you control.
The gap between an impressive demo and something your team trusts is evaluation and plumbing. We define what good answers look like before building, test against real questions from your business, log every response, and put a human in the loop wherever mistakes are expensive. When data cannot leave your environment, we deploy open models locally instead of sending it to a third-party API.
Process
How the project runs.
- Step 1
Use-case + data audit
Pick one job worth automating, list the data it needs, and write 20-50 real test questions that define success.
- Step 2
Prototype + evaluation
Build the retrieval or agent pipeline and score it against the test set. You see accuracy on your questions, not a scripted demo.
- Step 3
Integration
Connect to your tools (CRM, helpdesk, drive, database, Slack) with permissions, logging and human-in-the-loop steps.
- Step 4
Launch + monitoring
Roll out to a small group first, review logs, tune, then widen. Monitoring and cost tracking stay in place.
What's included
What you get.
- Use-case selection and a written success definition
- RAG pipeline over your documents, site or database
- AI agents with tool access, limits and approval steps
- Private or local LLM deployment when data must stay in-house
- Evaluation set and accuracy report before launch
- Conversation and action logs you can audit
- Cost controls and model choice explained in plain language
- Documentation so your team can maintain it
Why Kodeit
Why teams work with us.
Fixed scope, fixed price
You get a written scope and a fixed quote before any work starts. If something new comes up, we price it separately and you decide.
You own everything
Code, repositories, hosting, domains, data and every account we create are yours. No proprietary platform, no lock-in, full handover docs.
FAQ
Questions we get asked.
What is a RAG chatbot and does my business need one?
A RAG chatbot answers questions by first retrieving relevant passages from your own documents, then generating a reply grounded in them. You need one when staff or customers repeatedly ask questions whose answers already exist in your docs, policies or knowledge base.
Can I run an AI model privately on my own server?
Yes. Open models can run on your own hardware or private cloud so data never leaves your environment. We size the hardware, deploy the model and connect it to your data, and tell you honestly where a hosted model would perform better.
How much does a custom AI system cost?
Projects start from $2,000 for a single, well-defined use case. The fixed quote depends on data sources, integrations and whether the model is hosted or private, and ongoing model costs are estimated up front.
How do you stop the AI from making things up?
By grounding answers in retrieved sources, testing against a set of real questions before launch, showing citations where possible, and routing low-confidence or high-stakes cases to a human.
Which AI models do you use?
Whichever fits the job: hosted models from providers such as Anthropic and OpenAI, or open models run locally for privacy. The choice is documented with the trade-offs in cost, quality and data handling.
Start this project
Book a free consultation.
A free 30-minute call to discuss your project. No obligation, no pressure.