AI software development company

InterCode builds production-grade AI systems for B2B companies, including LLM-powered agents that autonomously complete multi-step tasks, RAG (Retrieval-Augmented Generation) pipelines that let your internal knowledge base answer employee or customer queries accurately, computer vision systems for inspection and classification, and predictive analytics platforms. We have shipped AI implementations across HR tech, real estate, outdoor advertising, and property management verticals. Our work spans model selection and fine-tuning, backend integration, evaluation pipelines to manage hallucination risk, and ongoing monitoring. We work with models from OpenAI, Anthropic, Google, and open-source providers depending on your requirements.

A focused AI agent — one that automates a specific, well-defined workflow — typically takes 6–10 weeks from scoping to production. This includes a 1-week discovery phase to map the workflow, a 2-week proof of concept to validate feasibility, and 3–7 weeks of production engineering and testing. More complex multi-agent systems with multiple handoffs and integrations run 3–6 months. One of our HR tech clients went from signed contract to a live AI recruiting agent in 8 weeks. The biggest variable is the quality of your existing data and API documentation — well-documented internal systems accelerate development significantly.

We work with the full spectrum of available models and frameworks. For LLMs, we primarily use GPT-4o and Claude Sonnet for production agents where reliability matters most, and open-source models like Llama 3 and Mistral where cost efficiency or data privacy requires on-premise deployment. For agent orchestration, we use LangGraph for stateful multi-agent workflows and n8n for lower-code automation pipelines. For vector storage and RAG, we use Pinecone, Weaviate, and pgvector depending on scale. We do not lock clients into a specific provider — our implementations are designed to swap the underlying model with minimal rework if a better option becomes available.

Every production AI system we build includes an evaluation pipeline that runs continuously against a curated set of test cases. For RAG systems, we use automated retrieval scoring to ensure the right chunks are being retrieved before the LLM generates a response. For agent systems, we build human-in-the-loop checkpoints for high-stakes decisions and automatic fallbacks to human agents when confidence scores drop below a threshold. One of our clients required that the AI system route any query about pricing or legal terms to a human reviewer — we implemented this with a classifier that intercepts relevant queries before they reach the LLM. We also provide weekly accuracy reports for the first 90 days post-launch.

A focused AI feature — such as an in-app assistant trained on your product documentation or a single-workflow automation agent — typically runs $25,000–$60,000 for initial development. Full AI platform builds (multi-agent systems, custom training pipelines, real-time data integrations) range from $80,000–$250,000 depending on complexity. Ongoing costs include model API fees (typically $200–$2,000/month at moderate scale), infrastructure, and maintenance. We offer both fixed-scope engagements for well-defined AI features and time-and-materials engagements for exploratory builds where requirements evolve. Contact us for a scoping call — we can typically give a rough estimate within one conversation.

Traditional automation (RPA, rule-based scripts) follows a fixed decision tree: if X, then Y. It breaks when inputs vary from the expected format. AI agents use language models to interpret intent and reason about the best next action — they handle ambiguity, incomplete information, and novel scenarios that would require constant rule updates in traditional automation. The practical difference: a traditional automation for processing invoices requires a perfectly formatted PDF every time. An AI agent can handle a scanned handwritten invoice, extract the key fields, flag anomalies, and ask a clarifying question if something looks wrong. One of our clients reduced manual data entry by over 70% after switching from a rules-based RPA system to an LLM-powered extraction agent.