Most teams have data, tools and a long list of ideas, but no reliable path from a prototype to an AI feature people actually use.
07 / AI Development Services
AI Development Services
Custom AI that works on your data, inside your product and your operation.
Machine learning and LLM-based systems built around your data and your workflows.
We start from one workflow where an AI feature would save real effort, and build a thin working version against your own data before widening scope. Every release is measured against a test set we agree on, so you can see whether the model is getting better or worse.
What the build includes
A complete production workflow.
Working AI feature in your product
A deployed feature behind your login, wired to your data and your existing screens, not a demo notebook.
Prompt, retrieval and model setup
The prompts, tools and model choices documented and versioned, so your engineers can change them safely.
Evaluation suite
A set of real examples with expected answers that runs on every change and flags regressions.
Production runbook
Logging, fallbacks, rate limits and an on-call guide for when a provider is slow or wrong.
Where it applies
Typical ai development services work.
- →Drafting replies from past tickets
- →Extracting fields from inbound documents
- →Ranking leads by fit
- →Answering staff questions from internal files
How a project runs
Four steps, no surprises.
- 01
Discovery call
A short call about the goal and the first workflow or feature worth building.
- 02
Written scope
What is built, which tools it touches, what a person approves, and the timeline, agreed before work starts.
- 03
Build and review
Short iterations connected to your tools, reviewed with your team before anything ships.
- 04
Launch and handoff
Go-live with documentation and an owner on your side. You own what we build.
Technology and fit
Built with the right tools, not every tool.
The exact stack follows the workflow, security requirements, existing systems, and deployment environment.
Technologies
OpenAIClaudeGeminiPythonFastAPIPyTorch
Industries
Expected outcomes
- →A feature your users actually open
- →Clear evidence of answer quality
- →Less manual review work
- →Model choices you can swap later
- →A team that can maintain it
Common questions
Before we scope it.
Do we need to pick a model before we start?
No. We compare a few candidates on your own examples and pick based on quality, speed and running cost. The code is written so the model can be replaced later.
What happens to our data?
It stays in your accounts or a private environment we set up with you. We use provider settings that keep your content out of training where the vendor offers that option.
How quickly can we see something working?
A first build can be live in 30 days for a focused workflow. Broader products are staged so you see working pieces along the way.
Keep exploring
Related services and technologies.
Free Audit
Get my free Audit ↗Know what to build first.
We review the workflow, recommend the first build, and give you a rough price range and timeline before you commit.