Ready to build AI into your business?
For companies building AI into their product or processes: ten questions on data, trust and production before the first model is chosen.
AI on your own data
Your AI is only as good as its data.
We build the data behind the answers.
An AI assistant your team can ask, a knowledge base that answers from your documents, an AI feature inside your product: each one depends on your own records, scattered across systems that were never built to feed a model. We are a senior engineering team working with AI. We bring that data in, make it consistent, make every answer traceable and testable, and run it in production on your own accounts.
The problem
AI on scattered data
answers fluently, and wrongly.
Your knowledge lives in CRMs, documents, spreadsheets, tickets and line-of-business systems, each with its own idea of a customer, a date or a status. A model reading that mix cannot tell which version is right.
A demo on ten clean documents looks perfect. On the real records, retrieval pulls the outdated policy or the duplicate record, and the answer sounds just as confident.
A successful request is not a correct answer. Without citations, a test set and review, nobody knows which answers to trust until someone acts on a wrong one. That is why our method gets the data right before the model does any work.
This is for you if
- You want an AI assistant or a RAG knowledge base that answers from your own documents and records, not from the open internet
- Your data lives in several systems that don’t agree with each other
- People will act on the answers, so every answer has to show where it came from
- Your data is sensitive or regulated, and it has to stay in your own accounts
What that looks like
The model is the last step.
The data is most of the work.
One model
Legal and health records from many systems, ingested and normalized into one validated model for a practitioner AI platform. The data layer was ours
Read the case study →HIPAA · SOC 2 · GDPR
Regulated data handled to those expectations, every record traceable from its source to the assistant’s context
Read the case study →1.6 s
A sales call scored against the client’s own scorecard by rules; AI scoring adds evidence quoted from the call
Read the case study →2,786 lessons
Written by an AI pipeline with AI in two of four stages, and the model chosen by measured quality
Read the case study →How it runs
Ingestion: start from the real data
Your documents, CRM, tickets, transcripts, databases and line-of-business systems, through their APIs and read-only wherever possible. One adapter per source, every call logged. Not a sample, not a demo dataset.
Normalization: one shape the AI can read
Every source mapped into one typed, validated model: identifiers, dates, duplicates and terminology resolved, documents split with the metadata retrieval needs. Records that don’t fit are rejected and logged, never passed to the model.
Trust: answers you can check
Structured outputs, rules alongside the model, citations back to the source document or record, and guardrails. A test set of questions with known answers is re-run on every change, and people review the answers that drive decisions.
Value: the job, not a chatbot
Retrieval-augmented generation over vector and hybrid search, an AI assistant, scoring or an AI feature in your product: we build what solves the problem, and choose the model by measured quality on your data.
Production: on your accounts, kept working
Your cloud, your AI provider, your repository. Access control, retries, monitoring, cost and latency control and traceable logs, so the system keeps working when a source changes or fails.
Questions teams ask
- Which AI models and vendors do you use?
- Whichever performs best on your data, measured, not assumed. We work with Claude, OpenAI and others, test the candidates against your own questions, and choose on quality, cost and speed. On one of our own projects, the cheapest model was the first tried and the first replaced. Nothing ties you to a single vendor.
- Where does our data live?
- In your own accounts: your cloud, your database, your vector store and your AI provider, from day one. Access is read-only wherever it can be, credentials are stored encrypted, and nothing is copied into a product of ours, because there isn’t one.
- How do we know the answers are right?
- We don’t assume it. Before launch we build a test set of real questions with known answers, run it against the system and re-run it on every change: that is LLM evaluation done on your data. Every answer cites the documents or records it came from, so anyone can check it, and the answers that drive decisions go through human review.
- Can it handle regulated or sensitive data?
- Yes. We have built the data layer for legal and health records to HIPAA, SOC 2 and GDPR expectations, with access control, credential handling, separation between customers and traceable logs treated as engineering requirements, not paperwork. Your compliance team sets the requirements; we build to them and document how.
- Who owns the code?
- You do, once it is paid for. All IP rights in the code we deliver transfer to you when every agreed payment has been made; until then, EVDevs keeps them. The code is built in your own repository and on your own accounts from the first day, and there is no product of ours underneath it to license or lock you in.
- What does it cost?
- Scope and price are agreed on the first call, once we know where your data lives and what the AI has to answer.
Want AI that answers
from your own data?
Tell us where your records and documents live and what your people need to ask them. We will tell you what it takes to make the answers trustworthy, and what we would build first.
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