We connect business data, software and workflows so intelligent systems can understand, reason, act and keep humans in control.
Systems designed around the operational problems your team needs to solve, not generic industry labels.
Lead research, qualification, proposal generation, CRM enrichment and follow-ups connected to the way your revenue team already works.
Support systems that retrieve knowledge, classify tickets, understand customer history, draft responses and escalate exceptions.
Document processing, approvals, reporting, reconciliation and internal tools that move structured work into the systems that need it.
RAG systems, enterprise search, knowledge bases, analytics and decision support grounded in your business context.
Attiv AI connects models to the systems where work actually happens. Each layer is observable, controllable and connected to a real business process.
Sometimes the right answer is a database query, API integration, conventional software or workflow automation. We use AI where reasoning creates leverage.
Reference systems that show how a manual process becomes a connected, reviewable workflow.
Sales teams research leads, check CRM data and decide who should be contacted.
Researches the lead, retrieves CRM context, evaluates criteria, enriches the CRM, routes the lead and requests approval where required.
Employees read documents manually and copy important fields into downstream systems.
Documents are ingested, structured data is extracted, anomalies are flagged and approved data is sent downstream.
Support agents search across multiple internal systems before responding.
Retrieves knowledge, checks customer context, drafts or executes the right action and escalates exceptions.
We move from process diagnosis to a production system with clear decisions, deliverables and control points.
Map the business process and identify high-value opportunities.
Process map + AI opportunity assessmentDesign integrations, data flow, security boundaries and human-control points.
Solution architecture + implementation planDevelop the system and integrate it with the existing technology stack.
Working production systemDeploy, monitor and document the system for the people who operate it.
Production deployment + observability + documentationImprove reliability, cost and usefulness from real system behavior.
Evaluation loop + system improvementsWe choose models, infrastructure and integrations according to the problem, then make the system understandable to the team operating it.
OpenAI, Anthropic, Gemini, Hugging Face
RAG, vector search, embeddings, MCP, agentic workflows
Python, Django, FastAPI
PostgreSQL, Redis, vector databases
AWS, Azure, GCP
Docker, Kubernetes, Terraform
n8n and workflow integrations
Engineering decisions that keep systems useful after the demo.
We solve the underlying system problem, not just the visible symptom.
Systems are designed around reliability, observability, security and maintainability.
We select models and infrastructure according to the problem rather than forcing one technology everywhere.
Systems can act autonomously where appropriate, while sensitive workflows retain explicit human approval.
Everything you need to know before we start building.
Tell us where your team loses time. We will map the process, identify where AI can create leverage, and outline what the first production system could look like.