Retrieval systems (RAG)
Retrieval systems that answer from your documents instead of inventing answers.
SERVICES
AI Development Services in the USA
Most AI projects fail somewhere between the demo and production. We build the part that survives contact with real users and real data.
- Evaluation built before the feature, not after complaints
- We tell you when a simpler approach wins
- Your data, your prompts, your evaluation suite
Start with a free consultation
Why Choose Softonoma for AI Development
Plenty of problems that get proposed as AI work are better solved with a rules engine, a search index, or a fixed form. We say so before the contract, because an AI feature nobody trusts costs more than the one you never built.
AI Development Services We Provide
Four kinds of AI work, sequenced by how much your data is ready for.
AI agents
Agents that take actions in your systems, with limits on what they can touch.
Machine learning
Classification, forecasting, and recommendation where classical models still win.
AI integration
AI features added to software you already run, without rebuilding it.
What is AI development?
What is AI development?
AI development means building software where part of the behavior is learned or generated rather than written out in advance. That shift moves most of the engineering effort somewhere unfamiliar — into data preparation, evaluation, and the guardrails that decide what the system is allowed to do when it's unsure.
Why AI Projects Fail to Reach Production
The demo is the easy part. A model answering ten questions well proves almost nothing about the thousandth. What separates a shipped feature from a stalled pilot is an evaluation suite built early, a defined behavior for low-confidence answers, and a person accountable for reviewing what the system produced.
RAG vs Fine-Tuning vs Prompting: Choosing an Approach
Prompting first, because it's cheapest to change. Retrieval when the answers live in your own documents. Fine-tuning when you need a consistent format or tone at volume. Classical machine learning when the task is prediction rather than language — and that's more often than the current conversation suggests.
AI Data Readiness Assessment
Your data has to be findable, reasonably clean, and permissioned before any of this works. Most AI engagements that stall do so here, which is why we audit it in discovery — usually alongside the back-end systems holding it. Sometimes the honest recommendation is to fix the data first and revisit the AI in six months.
AI Data Privacy and Security
Where your data goes, what leaves your systems, and what a model provider retains. We design for the answer your compliance team needs, and we tell you plainly which parts of a given approach aren't compatible with regulated data — which matters most for healthcare and fintech clients.
AI Development Cost
Discovery is fixed-price and produces a costed estimate. Data readiness drives the number more than model choice does. Running costs matter too — token spend, retrieval infrastructure, and the human review time an honest design includes.
Find Out What an AI Feature Will Cost
Request an estimateOur AI Development Process
How an idea about AI becomes a feature with measured behavior instead of hopeful behavior.
01
Discovery
We define the task, the data behind it, and what a correct answer looks like.
02
Planning
Approach chosen and an evaluation set built before any feature work starts.
03
Design
The interface designed around uncertainty, because the model will sometimes be wrong.
04
Development
Two-week sprints, each measured against the evaluation set, not against impressions.
Systems where we handle sensitive data at volume — in healthcare and fintech.
Healthcare Management System
A HIPAA-ready platform that pulled telemedicine, scheduling, and billing into one system for a multi-clinic network.
- React
- Node.js
- PostgreSQL
- AWS
Fintech Payment Gateway
A high-availability payment gateway that has processed over $500M in transactions at 99.99% reliability.
- Go
- PostgreSQL
- Redis
- Kubernetes
Mobile Banking Application
A banking app serving more than a million users, with biometric authentication and 99.9% uptime.
- React Native
- Node.js
- PostgreSQL
- AWS
Enterprise E-Commerce Platform
A headless commerce replatform that increased online sales by 150% and survived Black Friday without a wobble.
- Next.js
- TypeScript
- Node.js
- Elasticsearch
AI-Powered Analytics Dashboard
A real-time analytics platform that improved decision-making speed by 60% across production and supply chain.
- React
- Python
- TensorFlow
- Apache Kafka
Cloud Infrastructure Migration
A zero-downtime migration from on-premise data centers to AWS that cut infrastructure costs by 40%.
- AWS
- Terraform
- Kubernetes
- Docker
AI Development FAQs
Still have questions?
Talk to an engineerRAG or fine-tuning — which do we need?
Retrieval when the answers already exist in your documents and need finding. Fine-tuning when you need consistent format, tone, or structure at volume. They solve different problems, and retrieval is usually the cheaper place to start.
How do you handle hallucinations?
You design for them rather than pretending them away. Retrieval grounds answers in real sources, confidence thresholds route uncertain cases to a person, and the interface shows where an answer came from so users can check it.
Does our data go to a third-party model provider?
It depends on the approach and the provider. We map what leaves your environment, what's retained, and for how long, then design around whatever your compliance position requires — including keeping data in your own infrastructure.
Who owns what we build?
Everything. Prompts, evaluation sets, data pipelines, and application code live in your repositories from the first sprint. Model weights belong to whoever trained them, and we make that boundary explicit upfront.
Why does evaluation matter so much?
Because model behavior changes. Providers update models, your data shifts, and users find inputs nobody anticipated. An evaluation suite is how you notice before your customers do.