Already have a product, a workflow or a data source? We integrate Claude, GPT and Gemini into it properly: right model for the job, tool use and guardrails, evaluation, cost controls and monitoring. Fixed price, built by the founder, handed over with full IP.
By Dnyanesh Chandewar, Founder, CyberCore
Getting a model call working takes an afternoon. Getting it reliable inside your product or operations takes engineering: prompt and tool design, structured outputs, retries and fallbacks, rate limits, cost controls, evaluation, observability and security review. We handle that end to end, whether you need Claude via Anthropic's API, GPT via OpenAI's API, Gemini, or a multi-model setup behind one interface.
Typical work: adding AI features to your SaaS product (writing assistants, setup copilots, classification and extraction); connecting a model to your CRM, help desk, billing or internal databases through tool calls; building multi-step agent workflows with LangGraph or a lightweight custom loop; and migrating from one provider to another, or adding a second provider for resilience and cost. Where the use case is an autonomous agent, see AI agent development services; where it is question-answering over your documents, see RAG development.
We are not tied to a vendor. Accuracy on your actual tasks, latency, cost at your volume, context length, tool-use reliability and data-residency requirements decide the choice, and we test candidates on a sample of your own data before committing. For a longer comparison, read Claude vs ChatGPT vs Gemini: which model should power your AI agents.
Scoping (free): one call, then a written scope with the systems involved, success criteria and a fixed price. Build: a working integration in your sandbox early, with permissions, logging and cost controls designed in. Evaluation and hand-over: tested on your real cases, deployed to your environment, with code, prompts, evaluation suite and runbook handed over. Most focused integrations take 2–4 weeks; multi-system builds take longer and the timeline is agreed in writing first.
Hourly billing rewards scope creep, and LLM projects are especially prone to it because everyone has one more idea once they see it working. A fixed price forces both sides to agree what done means. If it is not a fit for fixed scope, we say so on the scoping call.
Every integration is quoted at a fixed price after a free scoping call, based on the number of systems involved, the reliability and evaluation required, and how much the model may do on its own.
Most focused integrations go from scope to production in 2–4 weeks. Multi-system integrations take longer, and the timeline is agreed in the written scope.
No. We work with Claude, GPT and Gemini and choose per use case, testing candidates on your own data first. We can also set up a second provider as a fallback.
Yes, where multi-step, stateful agent workflows need it. For simpler flows a lightweight custom loop is often more maintainable, and we recommend whichever fits.
You do. We hand over code, prompts, evaluation suite and documentation, deployed in your own infrastructure.
Book a 30-minute call. You'll leave with a clear view of what it would take and a fixed price if it makes sense.
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