Unclear problem definition
"Add AI" is not a requirement. Without a specific task, a success measure and a baseline to beat, there is no way to know whether the project worked.
AI & Automation · Software Development
AI software development services built around a measurable problem
We help you decide whether AI is the right tool, prove it on your own data, and then engineer the full product around it: interfaces, integrations, monitoring and the human review steps that keep it trustworthy.
What is AI Software Development?
AI software development services cover the full engineering of software in which a model makes or assists a decision, from scoping and data work through to a maintained production system. That model can be a large language model accessed through an API, an open-weight model you host yourself, or a classical machine learning model trained on your own records. The model is usually a small part of the work; the surrounding application, data pipelines, evaluation and operations are what make it usable.
Many AI initiatives stall between a promising demo and a dependable product. A prototype that answers ten hand-picked questions well says little about how it behaves on thousands of real inputs, what it costs per request, how it fails, or who checks its output. Treating AI as ordinary software engineering, with requirements, tests, versioning and monitoring, is what turns an experiment into something a business can rely on.
Why AI projects stall after the demo
Data that isn't ready
Records are scattered, inconsistently labeled or missing the fields a model needs. Data preparation is often the largest part of the effort.
Demo-quality evaluation
Testing on a handful of examples hides failure modes. Production systems need a representative test set and a repeatable way to score outputs.
Running costs and latency
Per-request model fees, GPU hosting and slow responses can make a technically sound feature impractical unless they are designed for from the start.
Our AI Software Development service
Feasibility and approach selection
A short discovery to define the task, the data available, the success measure, and whether a rules-based approach, a classical ML model or a language model is the better fit.
Data preparation and pipelines
Extraction, cleaning, labeling workflows and repeatable pipelines that feed training, retrieval or inference with consistent, traceable data.
Model integration or training
Integrating hosted models from providers such as OpenAI, Anthropic or Google, self-hosting open-weight models, or training classical models for classification, forecasting and scoring.
Evaluation harness
Test datasets, scoring scripts and regression checks so every prompt, model or data change is measured against the same baseline before release.
Application and integration engineering
The web app, API, admin tools and connections to your ERP, CRM or databases that put the AI capability in front of the people who need it.
Production operations
Logging, cost tracking, monitoring of output quality, model and prompt versioning, and a support arrangement for changes after launch.
Included capabilities
- Problem framing with a written success measure
- Baseline comparison against a non-AI approach
- Classical ML for classification and forecasting
- LLM features with retrieval and guardrails
- Representative evaluation datasets
- Human review queues for low-confidence outputs
- Per-request cost and latency tracking
- Versioned prompts, models and datasets
- Admin screens for reviewing and correcting outputs
What AI Software Development changes for your business
Decisions based on evidence
A prototype scored on real data shows whether the idea works before the larger investment is made.
Fewer surprises in production
Evaluation sets and staged rollout surface failure modes while they are still cheap to fix.
Predictable running costs
Cost per request is measured during the build, so model choice and caching are decided with numbers.
One team for the whole product
The same engineers handle the model, the application and the integrations, so nothing falls between vendors.
Where it makes the biggest difference
Internal decision support
Scoring leads, flagging risky orders or prioritizing maintenance work, with the model's output presented as a recommendation a person confirms.
Language features in an existing product
Summaries, drafting assistance or question answering added to a SaaS product or internal portal, grounded in the product's own data.
Demand and inventory forecasting
Classical time-series or gradient-boosted models trained on sales history, with forecasts reviewed by planners rather than applied blindly.
Document-heavy operations
Reading, classifying and extracting data from incoming documents, then routing exceptions to staff for review.
From problem statement to monitored production
There is an explicit decision point after the prototype, so you only fund the build once the approach has been proven on your data.
Frame
Define the task, users, success measure, constraints on data and cost, and what happens when the AI is wrong.
Assess data
Audit available data for coverage, quality and sensitivity, and agree what can be sent to external providers.
Prototype
A narrow proof of concept on real data, scored against the agreed measure rather than impressions.
Decide
Review results together and decide to proceed, change approach or stop, with the reasoning written down.
Engineer
Build the application, pipelines, integrations and review tooling around the chosen approach.
Evaluate
Run the full test set, check edge cases and failure behavior, and confirm cost and latency are acceptable.
Launch
Staged rollout to a limited group first, with monitoring and a clear rollback path.
Operate
Monitor quality and cost, collect corrections from reviewers, and improve the system in measured iterations.
What it connects to
AI Software Development
- Hosted LLM provider APIs
- Self-hosted open-weight models
- Vector databases and pgvector
- Odoo and other ERP systems
- CRM and helpdesk platforms
- Data warehouses and SQL databases
- Cloud object storage
- Internal REST and GraphQL APIs
Built with
Choosing the right kind of model
| Criterion | Rules / heuristics | Classical ML | Large language model |
|---|---|---|---|
| Best for | Stable, well-defined logic | Predictions from structured, historical data | Unstructured text, language and open-ended tasks |
| Data needed | Domain knowledge only | Labeled historical records | Instructions, examples and reference documents |
| Explainability | Fully transparent | Moderate, depends on model | Limited; outputs must be checked |
| Running cost | Very low | Low once trained | Per-request fees or GPU hosting |
| Main risk | Brittle when cases change | Drift as data changes | Plausible but incorrect output |
Security, quality and scale
Data minimization
Only the fields a task needs are sent to a model, and sensitive data can be masked or kept on self-hosted models.
Provider terms reviewed
We check each AI provider's data retention and training-use settings and configure accounts accordingly.
Access and audit logs
Role-based access to AI features, with logs of inputs, outputs and reviewer actions for traceability.
Separated environments
Development and evaluation use test or anonymized data; production credentials and data stay in production.
What working with us looks like
Willing to say AI is the wrong tool
If a rule, a report or a form solves the problem better, we will tell you before any model is involved.
Evaluation is part of the scope
Test sets and scoring are planned and budgeted from the start, not added when something goes wrong.
Full-stack delivery
Our team also builds web applications, APIs and Odoo integrations, so the AI feature lands inside the systems you already use.
Honest about limitations
We document where the system is likely to be wrong and design review steps around those cases.
What it costs
Fixed-price packages with the scope written down, paid stage by stage. Your project goes live once it's fully paid.
Related services, technologies and industries
Sources and further reading
Official documentation and guidance if you want to read more about this topic.
AI Software Development: questions we hear most
How much does AI software development cost?
Cost depends on the scope of the application around the model, the state of your data, the evaluation effort and the expected usage volume. Model provider fees or GPU hosting are separate running costs that scale with usage. We estimate after a short discovery, and a scoped prototype is usually the most sensible first commitment.
Do we need large amounts of data to use AI?
Not always. Language-model features can often work with instructions, a few examples and your reference documents, while classical machine learning usually needs a meaningful volume of labeled historical records. The discovery phase establishes which situation you are in.
Which AI model will you use?
We choose the model per task after testing, not in advance. Options include hosted models from providers such as OpenAI, Anthropic or Google, open-weight models you host yourself, and classical models trained on your data. The choice balances output quality on your test set, cost, latency and data-handling requirements.
How accurate will the AI be?
We cannot promise an accuracy figure before testing on your data, and anyone who does is guessing. We agree a success measure with you, score the prototype against it, and report the result honestly, including the cases where the system fails.
Is our data used to train the provider's models?
That depends on the provider and the account settings, which we review before sending any data. Many providers offer business or API terms that exclude customer data from training, and self-hosted models keep data entirely within your infrastructure.
Who maintains the system after launch?
We can continue with monitoring, prompt and model updates, and feature work under a support arrangement, or hand over to your team with documentation. AI systems need ongoing attention because providers update models and your data changes over time.
Next step
Bring us the problem, not the model
Describe the task you want AI to help with and the data you have. We'll tell you whether AI is a good fit and what a sensible first prototype looks like.