For the case that already earned its build
AI Software Development
A validated AI case still has to become software that survives production traffic, bad inputs and a model that occasionally guesses wrong. This practice carries that engineering: model choice, the application layer, evaluation and operations, whether the feature is new, added to a live product, or inherited from another team's prototype.
How a validated case becomes shipped software
Model selection, the application layer and the evaluation suite that proves it works.
90 secSample frame · video pending01Before any sprint
The build decision worth making explicit
Starting engineering on a case nobody has tested is a common, expensive mistake. Three checks decide whether a sprint should start now or wait.
Has the case already been proven?
Engineering a case nobody validated risks discovering in a sprint what a short consulting workshop would have found on paper.
Is the data ready for production use?
A model that worked on a sample data set can still fail once real production data and its edge cases arrive.
Who owns the evaluation bar before launch?
Shipping without an agreed accuracy threshold means nobody can say afterwards whether the feature actually works.
02Agreed before any sprint
The ledger that keeps the build honest
Every build fixes the split before work starts: what you bring, what we build, what comes back, and what never leaves your control regardless of engagement shape.
- Your side
You bring
- The validated use case and its success criteria
- Access to the data the feature depends on
- Any existing product it must plug into
- Our side
We build
- Model or platform selection matched to the case
- The application layer built around the model
- Evaluation harnesses that measure accuracy before launch
- Your side
You receive
- A production feature with measured accuracy
- An evaluation suite your team can rerun
- A deployment pipeline for future model updates
Never leaves you
- Product scope and every roadmap decision
- The underlying data and its usage rights
- Final sign-off before anything reaches users
Where the line sitsWe carry the engineering from model choice to shipped code. What ships, and when, stays a call only you make.
03Three drivers, not a rate card
What actually drives AI build cost
AI work is priced against the assessment, not a template, because these three factors change the effort involved by multiples.
Model and platform choice
A hosted model with a simple prompt costs less to integrate than a fine-tuned model trained on your own data.
Evaluation depth required
A basic accuracy check is fast. A full suite covering edge cases and regressions takes materially longer to build.
Integration with existing systems
A standalone feature ships faster than one wired into legacy systems with their own constraints and release calendars.
04Evidence and proof
AI features still running, still measured, months later.
Delivery proof here is a shipped feature with an evaluation suite the client's own team can still run, not a demo recording. Every record waits for written client approval before it replaces this preview.
Explore case studies
A defect-detection model went from prototype to production line in one quarter.
Nexa Foundry had a promising computer-vision prototype nobody trusted on the factory floor. Evaluation harnesses and a tested fallback path made the model safe to ship.
- Defect detection accuracy
- Measured weekly against baseline
- Line stoppages from model errors
- Zero since launch
A fraud-scoring feature shipped with an evaluation suite the team still runs.
Vela Ledger needed a scoring model that would not silently drift. The build included an evaluation harness their own engineers now run before every release.
- Evaluation runs
- Before every release, by their team
- Model drift incidents
- None reported since launch
We got a model and the harness that proves it still works after every deploy. Most vendors only hand over the first part.
Sample · pending client approval
05Read this first
When this build is the wrong starting point
Engineering a case nobody has tested would cost more than it earns us in trust. Two alternative starting points are marked before any sprint gets scoped.
- AI Consulting
The use case itself is still unproven
Rank and test it first, before committing engineering time to a case that might not survive contact with real data.
- Hire AI Developers
You need individual engineers, not a delivery team
If your team already owns the architecture and just needs capacity, hiring AI developers gets you there directly.
- Hire AI Engineers
You want engineers who join your own standards
Engineers who have shipped models before can join your reviews without your team losing ownership of the build.
| Shape | Best when | You direct | We carry |
|---|---|---|---|
| Scoped build | The case and architecture are settled | Priorities and sign-off | Model selection, engineering, evaluation |
| Delivery pod | The feature evolves with what launch teaches | Product decisions each cycle | Engineering, evaluation, releases |
| Team extension | Your leads already run AI delivery well | Architecture and standards | Named engineers and continuity |
06Straight answers
Asked before an AI sprint gets scoped.
01How is this different from AI consulting?
Consulting decides which case deserves funding. This practice engineers the chosen case into production software, from model selection through evaluation to a shipped, monitored feature. The two run as one accountable delivery chain.
02Do you build on our existing codebase?
Usually, yes. Most AI features live inside a product that already ships, so the build is engineered to fit your architecture, release process and existing constraints rather than replacing them.
03What if we already have data scientists?
This practice typically sits on the engineering side of that divide. Your scientists understand the model; the gap is often production software, evaluation and operations, which is what gets built here.
04How do you choose which model to use?
Against the case, not a default. Hosted models, open-source options and fine-tuned models are compared on accuracy, cost and data residency, and the choice is documented with its trade-offs.
05What does an AI software build cost?
Scoped work is priced against the model choice, evaluation depth and integration complexity from the assessment. Ongoing delivery is billed monthly against a scope that moves with what each release teaches.
06How do you handle a model that misbehaves in production?
Guardrails, confidence thresholds and human fallback paths are engineered in before launch, not added after an incident. Monitoring catches drift early, and a named owner reviews it on a set cadence.
07Can you take over an AI prototype another team built?
Yes, starting with an honest assessment rather than a rewrite pitch. Many inherited prototypes need targeted fixes to evaluation or guardrails, not a full rebuild, and the assessment says which applies.