Add AI Intelligence to Your Existing Software

Every AI integration project starts with a discovery session. In it, we map your current software stack, identify the highest-value integration point, and define success metrics. We then design the integration architecture — selecting the right AI model, data pipeline, and security approach for your requirements.

We build iteratively. We start with a focused proof of concept that shows value within 2–3 weeks. Then we expand to the full integration with monitoring, error handling, and cost controls in place. Every integration is delivered with documentation and a handover session so your team understands how to maintain and extend it.

In practice the work follows five stages:

• discovery (mapping your stack and choosing the highest-ROI integration point)
• architecture (model selection, data flow, security review)
• proof of concept (a working slice on real data)
• production build (error handling, rate limiting, cost monitoring, evaluation tests)
• handover (documentation, training, and an optional support retainer)

Most clients see their first working AI feature in under three weeks. Because we integrate into the systems you already run, there is no migration, no retraining of staff on new tools, and no disruption to existing workflows.

Model choice is an engineering decision, not a brand preference. It is rarely permanent, because a well-architected integration keeps the model swappable behind an abstraction layer. The way we think about it:

OpenAI (GPT-4o family) remains the default for broad general-purpose tasks, multimodal inputs, and the largest ecosystem of tooling. Anthropic Claude is our pick for long-document analysis, complex reasoning, agent workflows, and tasks where instruction-following precision and lower hallucination rates matter. It also pairs naturally with the Model Context Protocol (MCP) for connecting AI to your internal tools. Google Gemini is strong on very long context windows and Google Workspace-adjacent integrations. Self-hosted open models (LLaMA, Mistral) make sense when data may not leave your infrastructure. They also fit when very high request volumes make per-token pricing uneconomical.

Cost varies by an order of magnitude between model tiers. Most production integrations route easy requests to cheaper, faster models while reserving frontier models for the hard ones. We benchmark on your actual workload during the proof of concept. See our Claude API integration guide and OpenAI vs Anthropic vs Gemini comparison for how we evaluate.

Cost is driven by three factors:

• integration depth (a single API feature vs. a multi-system agent workflow)
• data complexity (clean structured data vs. messy documents needing custom extraction)
• compliance requirements (consumer-grade vs. regulated-industry architecture)

A focused single-feature integration sits at the lower end and ships in 1–3 weeks. Multi-system RAG and agent deployments are larger engagements over 4–12 weeks. Every project is fixed-price and scoped upfront — no hourly surprises.

On returns: across the 1,000+ projects we have delivered since 2015, AI automation projects typically pay back their build cost within 3–6 months. Teams reclaim roughly 40 hours per week on previously manual workflows. We published our full benchmark data in our AI Automation ROI Benchmarks report. It covers ROI ranges, payback periods, and deflection rates by use case. You can use it to sanity-check any vendor's promises, including ours.