AI agents for business, built to run in production.
Most agent projects stall between the demo and the deploy. We build the other kind: tool-using agents with memory, retrieval, evals, and orchestration around a real workflow — designed by software architects with 26+ years of shipping and deployed to your cloud or your own hardware.
Start with one workflow that costs you real hours.
Customer operations
Agents that triage tickets, pull order and account context from your systems, draft grounded replies, and escalate with a full trail of what they did and why.
Sales and research
Prospect research, enrichment, and briefing agents that read your CRM, the public web, and your own documents, then hand a human a decision instead of a link dump.
Internal automation
Back-office workflows — reconciliation, reporting, data cleanup, onboarding checklists — run by agents with narrow tool permissions and approval gates.
Knowledge retrieval
Private semantic search over your contracts, tickets, wikis, and code using local embeddings and reranking, so answers cite your material and not the open internet.
Content and creative pipelines
Generative pipelines for text, image, and video fine-tuned to your brand and catalog, including Stable Diffusion, Flux, and LoRA training deployed where you need it.
Agentic product features
Autonomous assistance built into the product your customers already use, with the latency, cost, and failure behavior worked out before launch.
A prompt is not a system. This is what a business agent needs.
Memory
Short-term working state plus durable memory, so an agent knows what it already tried and what your business already decided.
Embeddings and reranking
Retrieval that actually returns the right passage. Local embeddings and reranking keep private data private and inference costs predictable.
Tools and MCP servers
Typed tool interfaces over your APIs and databases, exposed through MCP servers so the same capabilities are reusable across agents.
Workflows and orchestration
Deterministic scaffolding around non-deterministic models: retries, branching, queues, human approval steps, and idempotent side effects.
Evals and observability
A test suite for behavior. Every step logged, every regression caught before it reaches a customer.
Local or cloud inference
OpenAI, Anthropic, and Google Gemini when the cloud is right; on-prem and on-device models when it is not. Often both, behind one interface.
The stack we reach for: OpenAI, Codex, Anthropic, Claude Code, Google Gemini, Runpod, OpenClaw, dynamic GPU clouds on H100s, A6000s, and T4s, and local models running on-prem or on your machine. We pick per workload instead of committing your business to one vendor's roadmap.
We ship agentic products, not agent slide decks.
GridRocket is agent infrastructure we built as a product: one click provisions a machine, installs the agent, issues an HTTPS domain and a private access token — Linux and Windows, flat monthly pricing, no DevOps. It is the fastest way to put a real agent into production on dedicated cloud hardware.
PasClaw is an AI agent we orchestrated with memory, embeddings, workflows, reranking, local models, and MCP servers — the same architecture we bring to business deployments.
Across 26 years we've produced technology reaching 250M+ users and 2M+ developers, and executed 300M+ AI operations. Every build on our featured builds page is live software you can open right now.
Faster time to market. Since 1999.
Scope one workflow
We pick the task with the clearest cost and the clearest success signal, and write the evals before the agent.
Ship to production
Real users, real data, narrow permissions, approval gates. We modify what already works instead of rebuilding it, so launch happens now instead of later.
Expand or hand off
Widen the agent's scope, or document and transfer it to your team. Fractional AI CTO support is available either way.
AI agents for business, answered.
What is an AI agent for business?
A system that plans multi-step work, calls your tools and APIs, reads your data through retrieval, and completes a task end to end instead of only answering a question. The useful ones are scoped to a single workflow and measured against evals.
How long does it take to deploy an AI agent?
We scope a first production agent around one workflow and target weeks, not quarters — by composing existing models, tooling, and infrastructure rather than rebuilding them.
Can AI agents run on our own hardware?
Yes. We deploy local AI — models, embeddings, and reranking running on-prem or on your machines — for organizations that cannot send data to a third-party cloud.
How do you keep agents from going off the rails?
Narrow tool permissions, typed inputs and outputs, human approval gates on consequential actions, structured logging of every step, and an eval suite that runs before each change ships.