Practical AI features built into real business software — not AI for its own sake. Document processing, forecasting, recommendations, and automation, grounded in your actual data.
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Overview
A lot of "AI development" is hype dressed up as strategy. We focus on the practical end: using AI models (often via existing APIs like OpenAI or Anthropic, sometimes custom-trained) to solve specific business problems — extracting data from documents, forecasting demand, flagging anomalies, recommending products. If AI isn't actually the right tool for your problem, we'll tell you that instead of selling you a model you don't need.
What we do
Our approach
Process
A clear process with no hidden stages, so you always know what is happening and what comes next.
We start by understanding the actual business problem — often, this step reveals that a simpler rules-based solution works better than AI. We're honest about that.
AI is only as good as the data behind it. We assess what data you have, what's missing, and what quality issues need fixing before building anything.
We decide between using an existing AI API, fine-tuning an existing model, or a simpler non-AI approach — based on what actually solves your problem cost-effectively.
We build the feature and integrate it into your existing software, with proper error handling for when the AI gets something wrong.
We test against real cases, measure accuracy, and set realistic expectations — AI systems are rarely 100% accurate, and we design around that reality.
We monitor performance after launch since AI system accuracy can drift, and iterate based on real-world results.
Pricing
Every project is quoted on scope after a short discovery call. See our pricing page for standard website packages.
FAQ
Straight answers on cost, timelines and what happens after launch. Still unsure? Ask us on WhatsApp.
A focused feature using existing AI APIs (e.g., document extraction). A more complex system with custom logic or fine-tuning. Ongoing AI API usage costs are separate and scale with volume. We quote after understanding your requirements — see our pricing page for standard packages.
Most business use cases are well served by existing APIs (OpenAI, Anthropic, Google) rather than training a model from scratch, which is expensive and usually unnecessary. We only recommend custom training when there's a clear, specific reason existing models won't work for your case.
Sometimes not — and we'll tell you honestly. A lot of problems people think need AI are better solved with simpler rules-based logic that's cheaper, more predictable, and easier to debug. We assess this upfront before recommending an AI approach.
Depends on the use case and data quality — we won't promise 100% accuracy because it's rarely realistic. We design systems with human review for high-stakes decisions and set honest expectations about error rates upfront.
We consider this carefully, especially for sensitive business or customer data. Depending on your requirements, we can architect around data minimization, choose providers with appropriate data handling policies, or explore self-hosted model options.
Yes, this is one of the most practical AI use cases — extracting structured data from invoices, receipts, forms, or reports that would otherwise require manual data entry.
They can add up with high volume — we design with this in mind, using caching, batching, and cost-aware architecture, and we're upfront about expected ongoing API costs before you commit.
Yes. Retainers fromfor monitoring accuracy, handling edge cases, and iterating on the AI feature as your data and needs evolve. We quote after understanding your requirements — see our pricing page for standard packages.
Tell us about your project. We review your needs, recommend a solution and send a clear quote, usually within 24 hours.
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