AI services built for production
Our AI Services practice builds practical, production-grade AI systems, not demos.
Free call. If we are not the right fit, we will say so.
- 6+ AI pilots shipped
- 3+ evaluation harnesses
- AI pilots shipped
- 6+
- Evaluation harnesses
- 3+
- Guardrail reviews
- 4+
- Retrain loop
- 2 wk
Why it matters now
AI implementation gaps are widening competitive moats
AI agents handling repetitive workflows at scale
Teams spent hours on repetitive tasks that AI could handle, but lacked the technical capability to build safely. The result: production AI systems now run internal workflows, customer interactions, and data processing without manual intervention.
Why now
- GPT-4-class models now enable automation that was enterprise-only 18 months ago.
- RAG systems give businesses hallucination-controlled AI grounded in their own data.
- AI agents can replace repetitive analyst, support, and ops workflows.
- LLM integration complexity is dropping, but deployment expertise still matters.
What you get
What we build for AI services
A production-deployed AI system with documentation, monitoring, and optimization support.
AI Agent Development
Custom agents for support, operations, and internal automation.
RAG Knowledge Systems
Retrieval-augmented generation grounded in your own data.
LLM Integration
GPT and Claude integration into existing products and workflows.
Included in engagement
- AI use case discovery and ROI prioritization
- Agent or RAG system architecture and build
- Production deployment with logging, error handling, and monitoring
- Optimization report after 30 days of live performance data
- OpenAI / GPT-4o
- Anthropic Claude
- LangChain
- RAG Pipelines
- n8n
- Custom Agents
Scoped by system complexity, integration count, and deployment environment.
How it runs
How we deliver production-grade AI systems
- 01
Use Case Discovery
Identify the highest-ROI AI opportunities across your operations and product.
- 02
Architecture Design
Choose the right model, retrieval strategy, and tool integration for each use case.
- 03
Prototype & Validate
Build a working prototype and validate accuracy, latency, and guardrails.
- 04
Production Build
Harden the system with error handling, rate limiting, logging, and monitoring.
- 05
Deploy & Optimize
Launch to users and iterate based on real-world performance and feedback.
Real client, real build
What a client says after launch
Passivae
- Full-stack SaaS
- Marketing automation
- Agentic systems
- DevOps
Full stack, automation, agent workflows, and infra in one loop. No hero deploys. No mystery config. If something breaks at 2 a.m., we know exactly which repo to open.
Before you start
Questions teams ask before starting
What kind of AI systems do you build?
We build support agents, internal knowledge bots, RAG systems, data extraction pipelines, and workflow automation agents, anything that replaces repetitive human tasks.
How do you prevent hallucinations?
We use retrieval-augmented generation (RAG), structured prompts, output validation, and human escalation routing to control AI output quality.
Do you use OpenAI or can we bring our own model?
We work with OpenAI, Anthropic, and open-source models. We recommend the right model based on your privacy requirements, latency needs, and budget.
How long does an AI system take to build?
A production-ready AI agent typically takes 4-8 weeks to design, build, test, and deploy depending on integration complexity.
Put the next stage on autopilot
Book 30 minutes. Leave knowing what turns on first.
Tell us what runs today and what breaks. We do the rest of the thinking on the call.
your AI services
Pick your time
What happens on the call
- You tell us what runs today and what breaks
- We point at the manual glue worth removing
- You leave knowing what turns on first
- Free
- 30 minutes
- Not the right fit? We will say so
“I sleep better anyway.”
“No hero deploys. No mystery config.”
“Quiet handoffs. No fog about who owns what.”