Assistant on company data
The model answers questions based on your documents, databases and decisions (RAG) — with references to sources, not guesses.
Integrations · AI/LLM
AI/LLM integrations wired into your systems, data and processes
Plain ChatGPT doesn't know your company. We connect language models to your data and systems — through APIs, RAG and CRM/ERP integrations — so they answer based on real data and run inside your processes.
Free, no strings attached. At the end you get a PDF report breaking down your project.
The problem
Plain ChatGPT doesn't know your company, data or systems
Language models look impressive in a chat window, but until they have access to your data and systems they only answer in generalities. To deliver real value they need to be connected to your company's sources, wired into your processes, and kept in check for answer quality and predictable cost.
- the model doesn't know your documents, customers or decisions
- answers sound good but are sometimes made up
- data still has to be copied into and out of the chat by hand
- token costs grow and nobody keeps them in check
Who it's for
The biggest gains go to companies with their own data and systems
If you have data and systems worth connecting a model to, you can probably get real value out of them.
- Companies with an existing CRM/ERP
- SaaS and product companies
- Operations teams
- E-commerce
- Customer support teams
- Data teams
Example use cases
What we most often wire in
Document analysis and summarization
Contracts, offers, reports and emails go to the model, which extracts the key points and prepares concise summaries.
Content classification and tagging
Tickets, messages and records are automatically recognized, categorized and tagged for further handling.
Answer and content generation
Based on context and your knowledge base, the model drafts replies, descriptions and content ready to review.
CRM data enrichment
The model fills in and tidies up CRM records — summarizing contact history, extracting data from emails and suggesting next steps.
Wiring a model into your app via API
We add AI features straight into your existing application through an API — without rewriting it from scratch.
How implementation works
From a use case to a working integration
01
We define the use case and metrics
We set what the model should do, on which data, and how we'll know it works well — before we wire anything in.
02
We pick the model and architecture
We choose the model and approach — prompting, RAG on your data, or fine-tuning — for quality, privacy and cost.
03
We integrate with systems via API
We wire the model into your systems and data through APIs and webhooks, so it runs inside your existing processes.
04
We test quality and cost, then ship
We check answers on real data, optimize cost and roll out the integration with monitoring in place.
Technologies
The tools we build this on
- OpenAI API
- Claude / Anthropic
- RAG
- REST / GraphQL
- Node.js
- Python
- PostgreSQL
- Webhooks
- AWS
Risks and limitations
We're upfront about what you need to watch for
Token costs
The bill grows with the number of requests and the size of the context. We optimize prompts and pick models so costs stay predictable.
Quality and hallucinations
Models can be wrong and make things up. We use answer evaluation, verification and references to sources where it's critical.
Data compliance
Company data calls for care. We mind privacy, access control and secure processing when choosing the model and architecture.
FAQ
Common questions about AI/LLM integrations
How is an integration different from plain ChatGPT?
Plain ChatGPT doesn't know your company, data or systems. An integration connects the model to your sources (documents, CRM, databases, APIs), so it answers based on your real company data, runs inside your processes and can be kept under control for quality, security and cost.
Which systems do you integrate with?
Most often with CRM/ERP, databases, document drives and your own apps via REST or GraphQL APIs. If a system exposes an API or webhooks, we can usually wire it in. Where there's no integration, we design an intermediate layer.
Which model should I choose?
It depends on the task, your quality requirements, data privacy and budget. We pick the model (e.g. OpenAI, Claude/Anthropic) and architecture — prompting, RAG on your data, or fine-tuning — to fit the specific use case, not the other way around.
How do you control costs?
Cost depends mainly on the number of requests and the size of the context (tokens). We optimize prompts, pick cheaper models where they're enough, cache repeatable requests and monitor usage so the bill stays predictable.
Next step
See how long your AI/LLM integration will take
Describe your use case in a few steps and get a preliminary breakdown into modules, effort and risks. Then we can go through the report together.