AI that works.
Across your
business.

Yes. We map the workflow first, then connect the required tools through n8n, APIs, webhooks, and databases. AI is used for tasks such as classification, extraction, summarization, or drafting; deterministic rules, approvals, retries, and alerts keep the workflow reliable.

Retrieval-augmented generation, or RAG, lets an LLM answer using your approved documents and data. It is useful when answers must reflect company-specific knowledge, include citations, respect permissions, and stay current as source content changes.

We build agentic systems, but autonomy is designed around risk. Low-risk actions can run automatically; sensitive actions can require policy checks or human approval. Tool access is bounded, inputs and outputs are validated, and every important action can be logged.

Yes. We select models based on quality, privacy, latency, context, deployment options, and cost. When useful, we create a model gateway so the product can route tasks across providers or change models without a full rebuild.

Yes. We can audit prompts, retrieval, agent logic, model choice, code quality, latency, cost, security, evaluation, and deployment. The goal is to preserve what works, fix the production gaps, and create a clear path to reliable usage.

We combine better task design, grounded retrieval, structured outputs, validation, constrained tools, confidence rules, and evaluation datasets. For high-risk workflows, the system can cite evidence, defer when uncertain, or request human review.

Yes. Common integrations include CRMs, helpdesks, email, Slack or Teams, cloud storage, internal databases, telephony, e-commerce platforms, and custom business software. We work with APIs, webhooks, queues, and secure service accounts.

Timing depends on data readiness, integration complexity, risk, and scope. We usually start with discovery and a focused prototype so important assumptions are tested early. After that, we provide a milestone plan for production rather than guessing before the system is understood.

Yes. Support can include monitoring, prompt and retrieval improvements, model changes, workflow updates, evaluation, incident response, cost optimization, new integrations, and product extensions as usage grows.