We build practical AI features into existing products and workflows — chatbots, recommendation systems, and automation that solves a specific business problem, not AI for its own sake.
A lot of "AI integration" work being sold right now is a feature bolted onto a product because it sounds impressive, not because it solves a problem. We approach it the other way: starting from a specific bottleneck — too many support tickets, manual data entry, inconsistent recommendations — and only reaching for AI/ML if it's genuinely the best tool for that problem. Sometimes a good rules-based system outperforms a model and costs a fraction to build and run; we'll tell you when that's the case.
Where a large language model fits the problem, we typically build on OpenAI's API rather than training models from scratch — this gets you production-quality results faster and avoids the ongoing cost of maintaining custom infrastructure most businesses don't need. For classification, prediction, or pattern-detection problems, we use established frameworks like TensorFlow, working with your existing data rather than requiring you to build a data pipeline from zero.
A meaningful part of this work is grounding: making sure a chatbot or assistant answers from your actual product documentation and policies rather than hallucinating plausible-sounding but wrong answers. This matters more for reliability than most of the "AI feature" marketing acknowledges.
Clients typically come to this with one of a few goals: reducing repetitive support ticket volume with a chatbot that handles the common questions accurately, adding recommendations to an e-commerce or content platform to lift engagement, or automating a manual data-entry or categorization task that's currently eating staff hours. We'll help you figure out which approach actually moves the number you care about before any development starts.
Most AI features need reasonably clean, structured data to be useful — a recommendation engine needs user interaction history, an NLP classifier needs labeled examples. Part of our discovery process is assessing whether your existing data supports what you want to build, and if not, what the smallest viable data-collection step looks like before investing further.
A single well-scoped AI feature added to an existing product (a support chatbot, a basic recommendation widget) is often quoted within our full-product tier, starting from $4,000. Larger AI-driven platforms or automation projects spanning multiple systems fall under our enterprise tier with custom scoping. See pricing details.
Part of doing this work honestly is being clear about limitations. AI features are probabilistic, not deterministic — a chatbot can occasionally give a wrong or oddly-phrased answer even when well-built, and a recommendation model needs a meaningful volume of data before it outperforms simple rules. If your data volume is low, or the task genuinely needs 100% predictable behavior, we'll tell you that upfront rather than building something that looks impressive in a demo and frustrates users in production.
Generally no, for features built on existing APIs like OpenAI's models — these are maintained as part of a normal application and don't require specialized in-house ML expertise to keep running. Custom-trained models are a different story and we'll flag that distinction during scoping if it applies to your project.
It varies by use case. A support chatbot grounded in your documentation can work well from day one. A recommendation engine or predictive model typically needs a meaningful volume of historical user interaction data — we'll assess what you have during discovery and tell you plainly if it's not enough yet.
This is a real consideration, particularly for sensitive data. We review what's being sent to any third-party AI provider and can implement data minimization, anonymization, or self-hosted alternatives where privacy requirements demand it.