The problem
AI is on the roadmap. A demo bolted onto the side of the product leaves operators and users working exactly where they were. You need intelligence in the software that already exists, or in a new product that ships to real users.
AI Development: intelligence inside the product that already has a job.
Put AI where operators and users actually work: inside the software you already run, or inside a new product that ships.
Ask about a build
Start with the outcome.
We connect to your systems and get the right context, from customer records and internal docs to product data, so the model can do useful work with what you already have.
We evaluate with real tasks and your data, so responses are accurate, consistent, and safe before anyone sees them.
Retrieval that fits your business.
- Customer
- Jessica R.
- Status
- Shipped
- Delivery date
- April 24
- Items
- Atlas Runner, Mist
Response checks
Uses the connected order
Includes the delivery date
Waits for human approval
How we approach it
We start from the outcome. What should the system decide, draft, retrieve, or automate? Then we build the thinnest integration that proves it inside your product, with APIs, workflows, evaluation, and guardrails included as needed. The rhythm matches the rest of the studio: a set date, an open build, and work that ships in weeks.
Who it's for
Teams putting AI into existing products. Teams shipping an AI-backed product from zero. Teams pairing AI work with Custom Software so the model layer and the product layer ship together.
What this covers in practice
AI-native apps
Products where AI is the core loop. From brief to something users can open.
Evals from real traces
The first eval set comes from last week's failures. Twenty to fifty real traces, clustered in plain language, with one fixture per failure mode filed next to the feature. Deterministic checks come first: did the row update, did the tool fire once, did the payload match the schema. A model grader comes in only where two reviewers already agree. You end up with a set you can regress, grown from the traces you already have.
Shadow mode before a write
When the risk is a write, the new path runs on live inputs with a dry-run tool surface while the current path still owns the system of record. We compare what each path did, read the disagreements, fix the clusters, and promote one write type at a time. The model stays dual-path until each write earns promotion.
Custom model fine-tuning, as the third move
Custom model fine-tuning lives here, under AI Development. Prompt comes first. Retrieval comes next, when the model is guessing because it can't see the record. Weights come third, once the behavior holds steady and the evals show a plateau. We take on fine-tuning when prompt and retrieval have a ceiling you can show. Bring the fixtures and the date.
Have a workflow and a date? Ask about a build.
How AI Development pairs with other offers
AI usually lands inside Custom Software or an existing system. When the model needs new product around it, pair this with Custom Software. When your team owns the plan and needs more hands on the slice, add Staff Augmentation.
FAQ
Can you put AI into a product that already exists?
Yes. Putting AI into software that already exists is a core outcome, alongside greenfield AI apps. We focus on the workflow the model should change, then integrate the thinnest proven slice into your current system. Pair it with Custom Software when that system needs work too.
Do you stay on after launch?
If you want a build partner, yes. The default handoff leaves you running the product, and continuing is a conscious choice. Post-launch work often looks like Staff Augmentation or ongoing AI Development.
From Articles
- Golden cases from last week's failures: build the first eval set from real traces and cluster the errors before adding another judge.
- Shadow mode before the write: run the model on real traffic away from the system of record, compare, then promote.
- Fine-tune is usually the third move: prompt and retrieval first, weights after the evals show a plateau.
Want Always On Agents on the daily work too? We set them up in your own accounts, beside what we build. Tell us about the work.
How to start
Describe the workflow AI should change and the date it needs to be live. Ask about a build or write hello@mail.22atlas.com.