What are AI development services?
AI development services cover the software development, data work and engineering needed to take an AI idea to a production system: strategy, data pipelines, models or LLM features, AI system integration, testing and monitoring. Unlike an off-the-shelf AI tool, the result is built around your data and workflows.
What drives the cost and timeline of an AI PoC or MVP?
The main drivers are scope and number of user roles, integrations, data condition, design depth, compliance and security needs, and team shape. We do not quote a figure before discovery, because a discovery phase is what turns these drivers into an estimate.
Which engagement model fits my project?
Fixed price suits a clear scope, and time and materials suits work that will change as you learn. We usually recommend a fixed-scope proof of concept (PoC) or AI MVP first, then a dedicated team once the value is proven. Team extension fits companies that already have engineers.
How long does an AI project take?
It depends on data readiness, the number of integrations, compliance needs and how many user groups the first release serves. A PoC is shorter than an MVP, and both are shorter than a full production rollout. We give a timeline at the end of discovery, once we have seen your data.
How do I choose an AI development services provider?
Check for relevant case studies, a security certification such as ISO 27001, clear code ownership and IP assignment, a written statement of work and defined support terms. Red flags are accuracy promises before anyone has seen your data and no evaluation plan. See our guide to custom AI versus ready-made tools.
Is my data ready for AI?
A data readiness assessment checks sources, quality, access rights, labelling and pipelines against your use case. When data is messy, we clean and structure the part the first release needs and leave the rest for later. In some projects, the right first release is the data pipeline itself.
How are AI agents built?
We define the tasks an agent is allowed to do, give it scoped tools through MCP servers and add guardrails such as permission limits, input checks and human approval for risky actions. Every run is logged and tested against real cases. For knowledge tasks, our default is retrieval-augmented generation (RAG) before large language model fine-tuning, because it is easier to audit and update.
How do you control hallucinations in generative AI?
We ground generative AI systems in your own data with retrieval-augmented generation (RAG), so answers come from your documents and knowledge base and cite their sources. Before launch we test outputs against known-good answers, and in production we keep tracing them, so a wrong answer is caught and fixed. Chatguru, our open-source RAG chatbot framework, is built the same way.
How do you handle security, governance and compliance?
We apply role-based access, encryption and OWASP TOP 10 checks, and we work under ISO 27001 practices. GDPR and EU AI Act compliance start in discovery with risk classification and data processing records. Logging of prompts, tool calls and outputs makes every decision auditable.
Which industries use AI development services?
Typical use cases include clinical document summaries in healthcare and pharma, claims triage in insurance, product search in ecommerce, document checks in fintech and demand forecasting in logistics. Read more in our guide to practical AI use cases.
