Service · Artificial intelligence

AI development company for software that must work in production

We build agents, LLM products, RAG, document processing, and decision engines connected to your data and workflows. Not an isolated demo: operable software.

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AI development starts with the outcome, not the model

An AI system creates value only when it enters the workflow where work happens. We begin with the decision, document, conversation, or task that consumes time or limits the product, then decide whether it needs an LLM, a predictive model, conventional rules, or a combination.

Most production work surrounds the model: preparing data, respecting permissions, designing APIs, measuring quality, controlling cost, and defining what happens when confidence is low. That full-stack layer separates a demo from an operational system.

  • Tool-using AI agents with permissions and traceability
  • RAG and semantic search over private knowledge
  • Document extraction, processing, and classification
  • AI copilots and features inside SaaS applications
  • Scoring, prediction, and assisted decision workflows

AI developers with full-stack depth

An AI product needs more than prompts. It requires interfaces, authentication, APIs, databases, queues, observability, infrastructure, and a user experience that communicates limits and confidence.

We can own the product end to end or collaborate with your internal team where experience is missing. In either model, we work in a real repository, document decisions, and leave an architecture another engineer can maintain.

  • Product architecture and data models
  • Backend, APIs, and model orchestration
  • Web or mobile frontend
  • Evaluation, monitoring, and cost controls
  • CI/CD, security, and cloud deployment

From proof of concept to production without hiding risk

A proof of concept should answer whether the use case is feasible and whether quality justifies the investment. It should not become a disposable app no one can operate.

We define evaluation sets, edge cases, latency budgets, and cost per operation before scaling. A negative result is useful too: it prevents a company from building around a capability that is not reliable enough.

Experience applied to real operations

We have incorporated AI-assisted scoring into a 12-module financial platform, intelligent assistance into a 112-table cross-border commerce system, and automation across marketplace, payment, and logistics workflows.

Some client names remain under NDA, so we present technical scope, architecture decisions, and operational impact—the evidence an engineering buyer can actually evaluate.

See deployed AI and software systems →

Questions before hiring AI developers

We build tool-using agents, RAG systems grounded in private knowledge, document extraction and classification, AI capabilities inside SaaS products, scoring engines, and workflows combining language models with deterministic business rules.

Yes. We can own an end-to-end project or provide senior capacity for architecture, backend, data, RAG, evaluation, and deployment. Ownership of every deliverable, repository, and environment is defined before work starts.

Cost depends less on the model than on data readiness, integrations, security, and the reliability threshold. We first define the use case, success metrics, and operating cost before proposing scope.

No. We choose among OpenAI, Anthropic, Gemini, Azure OpenAI, AWS Bedrock, or open models based on privacy, latency, quality, and cost. The architecture avoids unnecessary provider lock-in.

We combine grounded retrieval, output validation, permissions, action limits, traceability, evaluations, and fallbacks. High-consequence tasks pair the model with business rules or human review.

You have an AI use case, but not yet a reliable architecture?

Describe the workflow, available data, and desired outcome. We will tell you what to validate first and what should not be automated yet.

Request an AI use-case review