AI Software Development Services

An AI software development company builds and ships production code, not just strategy documents. Our work covers everything from AI agent development to custom MCP implementations, agentic QA, and CI/CD integration, so AI systems are engineered with the same version control, testing, and review discipline as any other software.

Yes, this is one of our most common engagements. We integrate models, agents, and automation directly into your existing codebase, databases, and APIs, using AI integration patterns that don't require a rebuild. Most of the time, adding AI to what's already running delivers value faster than starting over.

Model Context Protocol (MCP) lets AI agents interact directly with your internal tools, databases, and systems, instead of being limited to answering questions. A custom implementation means we build the specific tool schemas, authentication, and permission boundaries your business needs, so agents can take real actions inside your existing infrastructure, following your business logic rather than generic defaults. Read more in our guide on AI agent frameworks.

Traditional QA checks whether a single response is correct. Agentic QA evaluates behavior across multi-step tasks: how an agent chains tool calls, handles edge cases, and recovers from failures. We also run regression testing over time, since a prompt change or model update can quietly alter agent behavior without anyone noticing until something breaks in production.

Agents introduce risk surfaces traditional application security doesn't cover, including prompt injection and unintended tool access. We implement guardrails, permission scoping, audit logging, and access controls purpose-built for agentic systems, aligned with the NIST AI Risk Management Framework, so every action an agent takes stays traceable for compliance.

Both. When an off-the-shelf model fits the problem, we integrate it. When it doesn't, our machine learning development team handles data preparation, feature engineering, training, and validation against your actual business metrics, with retraining and monitoring built in from the start so accuracy holds up as your data shifts.

AI tooling takes a lot of the repetitive work off your engineers, refactoring outdated architecture and assisting with framework migrations, while a human stays in the loop and supervises the changes. We extend the same approach into delivery, using agents inside your existing CI/CD pipeline for code review, test generation, and deployment tasks, without introducing untracked changes.

Cost depends on whether you're integrating AI into existing software or building new agentic systems from the ground up:

• Adding AI to existing software: $15,000 – $40,000
• Custom AI agent or MCP implementation: $30,000 – $90,000
• Full agentic system with QA, security, and CI/CD integration: $100,000+

• Python, TypeScript
• LangChain, LangGraph, CrewAI for agent orchestration
• OpenAI, Claude, Gemini APIs and open-source LLMs
• Pinecone, FAISS for RAG and vector search
• Existing CI/CD tooling (we integrate rather than replace)

Yes. Support includes model drift monitoring, agent behavior regression testing, security audits, and CI/CD pipeline maintenance, so the system keeps performing reliably as your data and usage patterns change over time.