AI Software Development
AI software that works with your data, systems, and people
OmniLabs builds machine learning models, generative AI copilots, and AI agents into the software your teams already use, trained and grounded on your business data, tested on real cases, and secured for production.
Discuss AI softwareWhere AI projects usually get stuck
- 01
Answers and signals are scattered across ERP, CRM, documents, and machine data, so people search, export, and guess instead of deciding.
- 02
A demo or a data-science notebook looked promising, but nobody knows how accurate it is on real cases, what it costs to run, or how to get it into production.
- 03
Models that worked at launch drift as customers, products, and suppliers change, and nobody notices until forecasts or answers go wrong.
- 04
Security and legal teams block AI because company data could leak, be kept by a model provider, or reach the wrong users.
End-to-end AI development, from use case to production
The model, its data, and the app your team uses, built and tested as one product.
- AI strategy and use-case selection
- AI agents, copilots, and voice assistants
- Generative AI and RAG on your data
- AI features for existing software
- Predictive models and computer vision
- Hyper-personalization and customer intelligence
From AI strategy to production-ready solutions
Practical AI that solves real business problems, built into the systems your team already uses.
Find where AI pays off
We map your business problems to the right AI solutions, so you know what to build, what to buy, and what to skip.
What you get:
- Use-case discovery and ROI estimates
- Data readiness and feasibility checks
- Compliance scope for regulated industries
- A delivery plan with clear milestones
Build AI agents and copilots
Production-ready agents that automate internal work and serve customers, starting with a focused pilot.
What you get:
- AI agents that automate business processes
- Voice agents for calls, bookings, and support
- Copilots that help employees in daily work
- Chatbots that hand off to a person when needed
Ship generative AI on your data
LLM apps built on GPT, Claude, Gemini, or Llama that answer from your own data, with sources you can check.
What you get:
- Knowledge assistants connected to company data
- Secure RAG with citations
- Fine-tuning and prompt engineering
- Cost control with caching and model routing
Add AI to the software you have
We add AI to your SaaS, ERP, or CRM without a rewrite, and fix the gaps that keep AI from working.
What you get:
- Copilots inside your web and mobile apps
- Legacy app modernization for AI
- Systems and APIs ready for AI workflows
- Data unified across platforms
Predict, classify, and inspect
Custom models trained on your data, with results decision-makers can understand and trust.
What you get:
- Forecasting, churn, and lead scoring
- Fraud and anomaly detection
- Document extraction and NLP
- Visual inspection and object counting
Build a hyper-personalized recommendation engine
Hyper-personalized recommendation engines that learn from each user and show the right product, content, or offer in real time.
What you get:
- Product, content, and offer recommendations
- Real-time personalized search, feeds, and emails
- Customer intelligence from live behavior
- A/B tests that measure revenue lift
What production-ready AI gives you
Faster, better decisions, as people get forecasts, risk scores, and cited answers inside the systems they already use.
Less manual work, with AI reading documents, drafting replies and reports, and preparing actions for a person to approve.
AI you can trust and afford, measured against real cases, secured for your data, and monitored for cost and drift.
From AI idea to a feature people rely on
Find the right use case
Identify the users, the decision or task, the data available, and the metric that proves the AI is working.
Prototype in weeks
Build a working prototype on your real data in about three weeks and test it against representative cases.
Engineer for production
Integrate with your systems, add guardrails and human review, and set up evaluation, training pipelines, and monitoring.
Launch and improve
Release to users, track accuracy, cost, adoption, and drift, and retrain or tune models as your data changes.
Questions about AI software development
What’s the difference between machine learning and generative AI?
Machine learning models learn patterns from your historical data to predict or classify, such as forecasting demand or scoring churn risk. Generative AI uses large language models to read, write, and reason over text and images. Many products combine both, and we choose per task.
How much data do we need?
It depends on the task. Generative AI features can start from your existing documents and records without training data. Predictive models need enough history for the outcome you want to predict, often months to years. We check data readiness in the first weeks.
Do you fine-tune models or train custom ones?
Only when it pays off. Most assistants work best with retrieval over your data and well-tested prompts. We fine-tune or train custom models when you need domain language, a strict output format, lower cost at scale, or a prediction no general model can make.
Can you add AI to our existing software without rebuilding it?
Yes. Most projects add copilots, predictions, search, or document extraction to an existing SaaS product, ERP, or CRM through its APIs and data, without a rewrite.
Is our company data safe with AI models?
We use provider options with zero data retention, or private deployments on Azure OpenAI, AWS Bedrock, or open-source models in your cloud. Access respects user permissions, and sensitive data can be redacted before it reaches a model.
How do you know the AI gives correct answers?
We build an evaluation suite from real questions, documents, and held-out data, measure accuracy and citation quality, and rerun it on every change. Assistants cite their sources and are designed to say when they don’t know.
What happens after go-live?
We monitor accuracy, latency, cost, and data drift, alert when quality drops, and retrain or adjust models as your business changes, so results stay reliable.
How quickly can we see something working?
A first prototype on your data usually takes about three weeks, and a validated proof of concept 6 to 8 weeks. A production release typically takes 3 to 6 months, depending on data and integrations.
Put AI to work on a real business problem
Tell us the decision or task, the people involved, and the data you have, and we’ll outline a prototype you can test on your own data.
