Solutions
Enterprise AI integration
We integrate language models into business systems with the controls enterprises expect: more than one provider, retrieval over your own documents, approval before action and measured cost.
Problems we work on
- Search and answers over internal documents — policies, contracts, technical manuals — with the source cited in every answer.
- Drafting and summarising inside an existing workflow, with a person approving the result before it is sent or stored.
- Support and back-office assistants that read from your systems through narrow, documented interfaces.
- An AI feature that already works but costs too much to run.
What we build
- A provider layer for OpenAI, Anthropic, Google and local models (for example via Ollama) behind one interface, with health checks and timeouts.
- Retrieval-augmented generation (RAG) over your documents: hybrid keyword and vector search, cited answers, separate workspaces.
- Multi-agent workflows with an evaluation step and human approval before any action with side effects.
- Token and cost accounting per request, so model spend is visible and controllable.
Working with your systems
An AI feature is only as useful as the systems it can reach, and only as safe as the permissions it is given.
- ERP, CRM and internal APIs are reached through service accounts you control, with read and write permissions defined per tool.
- Read-only by default: every write path is a separate, named tool behind an approval step.
- Credentials live in your secret store, never in prompts or source code.
- Deployment in your cloud account, or on your own hardware when data must not leave your network.
How quality is measured
Before go-live, an AI feature is tested against questions your own team writes, not against a demo script.
- An evaluation set built with your domain experts, including questions the system must refuse or answer with “not found”.
- For retrieval: whether the right source was found, whether the answer cites it, and how often an answer states something the source does not support.
- Token use and cost per request measured on the same set, so savings are numbers rather than estimates.
- The evaluation is rerun after every model or prompt change; a change that lowers the agreed score does not ship.
Your data and AI providers
- Provider choice is part of the design: each provider’s data-use and retention terms are checked and documented with you before real data is sent.
- Sensitive work can stay on local models (for example via Ollama); one provider layer lets a system use local and cloud models by task.
- Personal data can be masked before a request leaves your network where the use case allows it.
- Prompts and responses are logged only where you decide, for as long as you decide.
What you receive
- 01
Discovery
The use case, data sources, risks and a written scope with acceptance criteria and evaluation thresholds.
- 02
Build
Provider layer, retrieval or agent workflow, approval steps and the evaluation set, in your repository.
- 03
Acceptance
Evaluation results against the agreed thresholds, cost per request, and a runbook for operating and changing the system.
AI narrates; systems compute
Figures, distances, prices and dates come from deterministic code. Language models phrase them; they never invent them. EmlakKlip writes the narration of every video this way.
Frequently asked questions
- Can it run on local models only?
- Yes, where your hardware allows. The same provider layer serves local and cloud models, so a system can start fully local and add a cloud model only for tasks you approve.
- Will our data be used to train models?
- Not by us. For every cloud provider, its data-use terms are checked and documented with you before real data is sent; for the strictest cases, local models keep data on your hardware.
- How long does a first version take?
- It depends on your data and integrations. Discovery ends with a written scope and milestone plan, so you know the timeline before the build starts.
- Who owns the code, prompts and evaluation set?
- Once the related milestone is paid, the code, configuration and documentation are yours and are delivered to your repository. See enterprise engagement.
Measured, not assumed
In LLM Studio, our in-house AI workbench, routing each request to a single matching tool cut token use to 0.25× of Hermes Agent on the same 25 tasks, with all 25 succeeding. Read the case study.
Have a project in mind?
Send a short description. If you need a mutual NDA first, we will send ours before you share details.