Ship Autonomous AI Agents, Not Just Chatbots
We help startups design, build, and ship autonomous AI agents — systems that don't just answer questions, but actually get work done. From first prototype to production-grade agentic SaaS, we handle the engineering so you can focus on the product vision.
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From idea to autonomous product: how FeedbackRobot turned customer feedback into an AI agent that acts on its own, with under 2s AI latency.
Trusted by founders worldwide.
Read the full case study
Benefits
Autonomy With Guardrails
Your agents act independently, but always within limits you define — so automation never means losing control of your product or your data.
Built for Agentic-Scale Traffic
Agentic workloads behave differently than typical SaaS traffic. We architect for that from day one, so cost and performance stay predictable as adoption grows.
- Users served
- 2M+
- Products launched
- 30+
- Revenue generated
- $30M
users served
products launched
revenue generated
Own Your Agent Stack
Your agent's architecture, prompts, and model configuration are yours outright — fully portable, with no proprietary orchestration layer locking you to us.
What's Included
Built for Long-Term Success
From initial architecture to production-grade deployment — the full-stack expertise your startup needs to lead.
Agent Architecture & Design
We design the reasoning loop, tool access, and memory systems your agents need to complete real, multi-step tasks reliably.
LLM & Model Integration
We integrate and evaluate the right models for your use case, balancing cost, latency, and output quality.
Tool & API Orchestration
We connect your agents to the tools, APIs, and data sources they need to take real action, not just generate text.
Evaluation & Guardrails
We build testing and monitoring systems that catch bad agent behavior before your users do.
Agentic SaaS Infrastructure
We architect the backend, queues, and observability agentic products need to run reliably at scale.
Human-in-the-Loop Workflows
We design approval and escalation flows so humans stay in control of high-stakes decisions.
Our Approach
From Prototype to Production Agent
The structured, transparent steps we take to turn an agentic AI concept into a system your users can rely on.
Design the Agent
We map the tasks your agent needs to own, the tools it needs access to, and the boundaries it should never cross — before writing a line of code.
Integrate the Right Model
We evaluate models against your real use case — not a benchmark — and build the retrieval and memory systems that keep responses grounded.
Test & Add Guardrails
We stress-test agent behavior against edge cases and build monitoring that flags issues before they reach your users.
Launch & Operate
We deploy with real observability in place, then keep tuning cost, latency, and reliability as usage grows.
Pricing
Flexible Engagement Models
Whether you need to ship something new or keep something running, we have a model that fits.
Fixed Engagement
Best for new projects and ground up builds. We scope the work, set a clear timeline, and deliver end to end, from initial architecture all the way to launch.
What's included:
- Discovery & scoping workshop
- Full-stack architecture design
- Dedicated development sprint(s)
- QA, testing & deployment
- Handoff & onboarding
Ongoing Development
Best for teams that need a long term technical partner. We embed as your DevOps team and fractional CTO, keeping your product moving forward.
What's included:
- Monthly development retainer
- CI/CD pipeline management
- Infrastructure & cloud oversight
- Fractional CTO advisory
- Priority support & incident response
FAQ
Got questions? We've got answers.
What's the difference between a chatbot and an agent?
A chatbot answers questions. An agent takes action — it can call tools, update records, trigger workflows, and complete multi-step tasks on its own, within boundaries you define.
Which LLM should my agentic product use?
It depends on your use case, latency needs, and cost tolerance — there's no single right answer. We evaluate models against your actual workload rather than a generic benchmark, and design the system so switching providers later doesn't mean a rebuild.
How do you prevent an agent from taking the wrong action?
Through evaluation and guardrails built alongside the agent itself — scoped permissions, human-in-the-loop approval for high-stakes actions, and monitoring that flags unexpected behavior before it reaches your users.
Can you add agentic features to a product we've already built?
Yes — we regularly integrate agentic capabilities into existing products rather than starting from scratch, connecting agents to your current data and APIs.
How do you handle the unpredictable cost of agentic workloads?
Agentic workloads don't behave like typical SaaS traffic, so we architect for that from day one — rate limiting, caching, and monitoring that keeps cost and performance predictable as adoption grows.
Do we own the agent architecture and prompts you build?
Yes, outright — the architecture, prompts, and model configuration are yours, fully portable, with no proprietary orchestration layer locking you to us.
What happens when the underlying model provider changes their API?
We architect the integration layer so model calls are abstracted from the rest of your system — when a provider changes their API or you want to switch models, that's a contained change, not a rewrite.