AI Development Company

The right approach depends on what your AI system needs to do, what data you have, and what constraints you're operating under. RAG (retrieval-augmented generation): if you need to answer questions from your existing documents, knowledge base, or data, without training a model. AI agents: if you need to automate multi-step workflows where the AI needs to use tools, make decisions, and adapt to intermediate results. Fine-tuning: if you have a specific, narrow task and a labeled dataset, and a general model's accuracy isn't sufficient. Custom ML: if you have a prediction or classification problem, labeled historical data, and need a model trained on your specific data. We diagnose the right approach in a scoping session before recommending a build.

No. We use OpenAI (GPT-4o, GPT-4o mini), Anthropic (Claude 3.5 Sonnet, Claude 3.7), Google (Gemini 1.5 Pro, Gemini 2.0), Meta (Llama 3), and open-source models depending on what's right for the use case. Model selection is driven by performance on your task, cost at your volume, data residency requirements, and latency constraints. We have production experience with all the major frontier models and will tell you the trade-offs honestly, including when a cheaper or open-source model is a better fit than the most capable frontier model.

An AI proof of concept (POC) is a time-boxed build that answers a specific technical question: does this approach work on our data, with acceptable quality, at feasible cost? POCs make sense when: the task is novel enough that there's genuine uncertainty about whether AI can do it well; the data quality or availability is unknown; or there are compliance, latency, or cost requirements that need to be validated before a full build. We run focused 2-4 week POCs with a defined success criterion. If the POC succeeds, we scope the production build. If it doesn't, you've spent a fraction of what a failed production build would cost.

Quality in production requires evaluation infrastructure, not just good prompts. We build: evaluation datasets representing real query distribution, automated quality scoring using LLM-as-judge for qualitative outputs, regression testing to catch quality degradation when prompts or models change, production monitoring for output quality metrics over time, and human review queues for flagged outputs. Quality evaluation is not optional for production AI systems: it's the only way to know if the system is working.

Costs range significantly by scope. An AI proof of concept runs $8,000 to $20,000 for a 2-4 week focused investigation. A production AI feature integrated into an existing product runs $25,000 to $75,000. A standalone AI application with RAG, evaluation, and monitoring runs $50,000 to $150,000. A complex multi-agent system or custom ML pipeline runs $100,000 to $300,000+. We provide fixed-cost proposals after a scoping session, not hourly estimates that shift as scope changes.

We have shipped AI products for healthcare (HIPAA-compliant remote patient monitoring), financial services (fraud detection, document extraction for lending), logistics (route optimization, demand forecasting), retail (recommendation engines, dynamic pricing), and legal (contract review, document analysis). Most AI approaches, RAG, agents, ML classifiers, translate across industries because the underlying engineering patterns are similar. The domain knowledge that matters is understanding your data, your compliance constraints, and what good output looks like in your context.

Development time varies based on complexity. Simple AI apps typically take 1 to 2 months (6 to 8 weeks), while full-featured products can require 3 to 4 months (12 to 14 weeks). We work closely with you so your AI product meets your timeline expectations. Integration into an existing product typically runs 4 to 8 weeks depending on the complexity of your codebase and the AI capability being added.

Most of the AI work we do is integration into an existing product rather than a greenfield build. Common patterns include adding document processing to an existing workflow tool, embedding a support chatbot into a customer-facing product, or adding an AI layer to existing data pipelines for analytics or anomaly detection. The starting point is the same as a new build: a discovery session where we map your existing architecture, identify the integration points, and scope the work before any development starts.

Check the portfolio first. A company that has shipped AI in your industry understands the edge cases and compliance requirements you will actually hit. A company that has only shipped demos will discover them at your expense. After the portfolio, look at the process. Do they show you a working prototype before you commit to a full build? Do they lock the price before development starts? Do they stay available after launch? Finally, ask who builds the work. The team that pitches the project should be the team that builds it. Bait-and-switch, where senior engineers close the deal and junior contractors do the work, is common, so ask directly.

Data security is scoped in week 1, not retrofitted before launch. We have shipped HIPAA-compliant AI for US healthcare clients, SOC 2-compliant systems for financial services, and GDPR-aligned products for European markets. Standard practices across every project include data processing agreements before development starts, access controls and audit trails designed into the architecture from the first sprint, and clear documentation of where data flows, including which third-party models see what data. We sign NDAs before any technical conversation begins.