AI development company for AI that survives real customers.

You run a proof of concept first. It's a time-boxed 2-4 week build with one job: answer whether this works on your data, at acceptable quality, at feasible cost, against a success criterion we agree up front. It costs $9,500 to $20,000. If it clears the bar, we scope the production build. If it doesn't, you've learned the answer for a fraction of what a failed production build would have cost, and you walk away with the findings. Feasibility doubt is a reason to start with a POC, not a reason to wait.

The right approach depends on what the system needs to do, what data you have, and what you're constrained by. RAG (retrieval-augmented generation): when you need answers grounded in your existing documents, knowledge base, or data, without training a model. AI agents: when you need to automate a multi-step workflow where the AI uses tools, makes decisions, and adapts to what it finds. Fine-tuning: when you have a narrow task, a labeled dataset, and a general model isn't accurate enough. Custom ML: when you have a prediction or classification problem and labeled historical data. We work out the right approach in a scoping session before recommending a build, and we'll tell you when the honest answer is plain code or an off-the-shelf tool instead.

Fair question, and one worth asking every vendor. A wrapper is a prompt with a nice interface. Production AI is the part underneath: retrieval that grounds answers in your data, an evaluation harness that scores output against a golden dataset, monitoring that catches quality drift before your users do, and cost controls that keep the model bill predictable. Ask us for architecture diagrams, evaluation results, and the retros from AI systems we've shipped and still support. You also pay the model bills directly, on your own accounts, so a hidden markup on someone else's API isn't even possible here. If a vendor can only show you a demo, that's the tell.

No. We use OpenAI (GPT-4o, GPT-4o mini), Anthropic (Claude), Google (Gemini), Meta (Llama), 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. We have production experience across the major frontier models and will tell you the trade-offs honestly, including when a cheaper or open-source model is the better fit than the most capable frontier one.

This is the risk that ends up in the news, so we design against it from the start. One airline's chatbot invented a refund policy that didn't exist and a tribunal made the company honor it. That failure came from shipping a model with no guardrails, not from the model itself. Quality in production comes from evaluation infrastructure: evaluation datasets that represent your real query distribution, automated scoring with LLM-as-judge for qualitative output, regression testing to catch drops when a prompt or model changes, and production monitoring over time. For customer-facing systems we add guardrails and fallbacks, so the model refuses or escalates to a human instead of inventing an answer.

Costs range by scope. An AI proof of concept runs $9,500 to $20,000 for a 2-4 week investigation. A production AI feature inside an existing product runs $25,000 to $60,000. A standalone AI application with RAG, evaluation, and monitoring runs $55,000 to $150,000. A complex multi-agent system or custom ML pipeline runs $100,000 to $350,000. You get a fixed-cost proposal after a scoping session, not an hourly estimate that drifts as scope changes.

You do, all of it: the source code, any fine-tuned model weights, the prompts, the evaluation datasets, and the infrastructure configuration. Everything is deployed in your accounts and documented so your team can run and change it without us. There's no proprietary framework to license and no lock-in that forces a retainer. If you take the whole system in-house after launch, everything you need is already yours.

You pay the model and infrastructure bills directly, on your own accounts, so there's no markup and full visibility into what the system costs to run. What that bill looks like is a design decision we make with you: model choice (a smaller or open-source model wherever it performs well enough), caching and retrieval to cut redundant calls, and batching to control throughput cost. Token bills are famous for doubling unnoticed; we estimate the run-rate at your expected volume during scoping, so the monthly number is on the table before you commit, not a surprise after launch.

Check the portfolio first. A company that has shipped AI in your industry already knows the edge cases and compliance you'll hit. A company that has only shipped demos will discover them at your expense. Then 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 should be the team that builds. Bait-and-switch, where senior engineers close the deal and junior contractors do the work, is common enough that you should ask directly.

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

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

It varies with complexity. Simple AI apps typically take 1 to 2 months (6 to 8 weeks); full-featured products run 3 to 4 months (12 to 14 weeks). Adding AI to an existing product usually runs 4 to 8 weeks, depending on the codebase and the capability. We agree the timeline against your expectations before development starts, so the date isn't a moving target.

Most of the AI work we do is integration into an existing product, not a greenfield build. Common patterns: adding document processing to a workflow tool, embedding a support assistant 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 architecture, find the integration points, and scope the work before any development starts.

If AI is core to your product and you can hire and keep a senior ML and platform team, in-house is the right long-term answer. The gap is time. Hiring that team and building the evaluation, retrieval, and monitoring scaffolding around a model usually takes the better part of a year before the first production system ships. We bring that scaffolding and the production experience with us, ship the first system in weeks, and hand it over documented so your team can own it. Many clients use us to ship v1 and prove the value, then build the in-house team around a system that already works.