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Your Team Spent 14 Hours This Week on Tasks AI Could Handle in 14 Minutes

If you're a technical leader watching manual workflows drain engineering capacity, you've reached the automation inflection point.

We build autonomous AI agents with structured tool calling, custom workflows, and human-in-the-loop safeguards -- deployed on your infrastructure.

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< 2s
Agent Response Time
P95 latency target
40+
Agents Deployed
Across client projects
99.5%
Uptime SLA
Production environments
$0
Vendor Lock-in
You own the code
What Is AI Agent Development?

AI agent development is about building software that uses large language models to reason, plan, and execute multi-step tasks without constant hand-holding. These aren't chatbots. Agents call external tools -- APIs, databases, file systems -- to actually get things done. Custom workflows define the decision logic, guardrails, and approval checkpoints that keep everything from going sideways once it hits production.

Your Current Site May Be a Liability

Common gaps we find in nearly every audit.

Your chatbot answers questions but can't actually do anything
Risk: Users figure this out fast, and they leave.
LLM hallucinations are a real liability in customer-facing workflows
Risk: One wrong answer in finance or healthcare doesn't just embarrass you -- it can cost millions.
You've prototyped with ChatGPT but can't get it production-ready
Risk: The demo works great in staging while your competitors are already shipping.
Agent frameworks keep shifting -- LangChain, CrewAI, AutoGen -- and your engineers are burning time on framework churn instead of solving actual business problems.
Risk: Engineering time burned on framework churn instead of business logic
You've got no visibility into what the agent's doing or why
Risk: Black-box behavior makes debugging painful and audits nearly impossible.
Sensitive data can't leave your infrastructure for external API calls
Risk: Uncontrolled LLM traffic is a compliance violation waiting to happen.

What Your Website Could Look Like

Custom-designed for your industry. No templates. No stock photos.

AI agent platform with tool calling workflow visualizer and traces
A production AI agent build -- multi-step workflow visualizer, tool-call trace, prompt versioning, and per-step latency metrics

How We Build This Right

Every safeguard, built in from Day 1.

Structured Tool Calling

We define typed tool schemas so agents call your APIs with validated parameters -- no prompt-hacking required. Every tool invocation gets logged and is fully auditable.

Multi-Step Workflow Orchestration

Complex tasks get broken into deterministic workflow graphs with conditional branching. Agents follow defined paths while still reasoning through each individual step.

RAG Pipeline Integration

Retrieval-augmented generation keeps agent responses grounded in your actual data. We build vector search with pgvector and tune chunking strategies to match your specific content structure.

Human-in-the-Loop Safeguards

Critical actions don't execute until a human approves them. Configurable escalation rules give you precise control over exactly when the agent stops and asks for permission.

Full Observability & Tracing

Every agent run is traced end-to-end -- reasoning steps, tool calls, token usage, latency. Dashboards and alerting ship on day one, not as an afterthought.

Data Privacy & Self-Hosting

We'll deploy on your own infrastructure so no data leaves your VPC. Self-hosted LLMs and private API endpoints are fully supported for regulated industries.

What We Build

Purpose-built features for your industry.

Custom Tool Definitions

Typed, versioned tool schemas that connect agents to your existing APIs, databases, and internal services.

Conditional Workflow Graphs

Visual and code-defined workflow DAGs with branching, retry logic, and parallel execution.

Model-Agnostic Architecture

Swap between OpenAI, Anthropic, Mistral, or self-hosted models without rewriting your agent logic.

Streaming Response UI

Real-time streaming interfaces that show users exactly what the agent's doing as it works.

Evaluation & Testing Suite

Automated eval harnesses that test agent behavior against golden datasets before every deploy.

Cost & Token Management

Per-user and per-workflow token budgets with automatic model downgrade when limits get close.

Built on a Modern, Secure Stack

Next.jsVercelSupabaseOpenAIAnthropicLangChainVercel AI SDKPostgreSQLpgvector

Our Development Process

From discovery to launch. Quality at every step.

01

Agent Architecture Workshop

Week 1

We map your business processes to agent capabilities. You'll walk away with a workflow diagram, tool inventory, and risk assessment for every automated action.

02

Tool & Schema Development

Week 2-3

We build typed tool definitions, connect your APIs, and handle authentication and error handling for every external system the agent will touch.

03

Workflow & Prompt Engineering

Week 3-4

Multi-step workflows get built and tested in full. Prompts get engineered with structured outputs, few-shot examples, and guardrails against the failure modes we see most often.

04

Evaluation & Hardening

Week 5

We run the agent against adversarial test suites and measure accuracy, latency, and cost. Edge cases get documented and handled -- nothing goes to production until it does.

05

Deploy & Monitor

Week 6

Production deployment includes observability, alerting, and a 30-day support window. We also train your team on prompt tuning, eval maintenance, and scaling so you're not dependent on us forever.

Social Animal

Ready to discuss your your team spent 14 hours this week on tasks ai could handle in 14 minutes project?

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AI Agent Development from $12,000

Fixed-fee. 30-day post-launch support. See all packages →

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Frequently Asked Questions

A chatbot responds to messages. An AI agent reasons about a task, calls external tools — APIs, databases, file systems — and executes multi-step workflows on its own. We're talking booking appointments, processing refunds, generating reports, triggering real actions. Not just replying with text.
We're model-agnostic. Most projects end up using a mix — GPT-4o or Claude for complex reasoning, something lighter like GPT-4o-mini for simple classification steps. Our architecture lets you swap models per workflow step, so you're balancing cost and quality at the same time. We also support self-hosted models via Ollama or vLLM if you need to keep everything on-prem.
Three layers. Structured tool calling with typed schemas forces valid outputs. RAG pipelines keep responses grounded in your actual data. Human-in-the-loop checkpoints catch edge cases before high-stakes actions execute. On top of that, automated eval suites flag accuracy regressions before every deployment goes out.
Yes. Agents can connect to anything with an API — CRMs, ERPs, databases, email platforms, payment processors. We build typed tool definitions for each integration with proper authentication, rate limiting, and error handling baked in. No API? We can build one, or use browser automation as a bridge.
A focused single-workflow agent typically takes 4–6 weeks from kickoff to production. Multi-agent systems with several tool integrations and approval workflows usually run 8–12 weeks. Every project includes a 30-day post-launch window for prompt tuning and performance optimization.
When data sensitivity demands it, we deploy entirely on your infrastructure. For regulated industries, agents can run inside your VPC with nothing leaving your network. We support private LLM endpoints, encrypt all data at rest and in transit, and give you full audit logs of every agent action and tool call.
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