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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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AI Agent Development

AI agent development is the practice of building software systems that can autonomously plan, use tools, and complete multi-step tasks -- all with minimal hand-holding from your team. And that last part matters more than most people realize. We're not talking about chatbots you buy off the shelf and configure with a few dropdowns. Custom agents get wired directly into your actual data sources, your internal APIs, your approval workflows -- the messy, specific infrastructure that makes your business run. The result is a production system that lives inside your security boundary, plays by your access controls, and actually understands your domain-specific business logic instead of giving you generic answers that need a human to finish the job anyway. Think of it less like deploying software and more like onboarding a very fast, very literal employee who never sleeps, never loses context mid-task, and -- crucially -- only does exactly what you've defined and approved. That last guardrail is what separates a real production agent from a impressive demo that falls apart the moment something unexpected happens.

What is holding your current website back?

Common gaps we find in nearly every audit.

Here's the thing -- most engineering teams I talk to are running on duct tape
Risk: Cron jobs that fail silently at 2am, Zapier flows that break when an upstream API changes a field name, one-off Python scripts that only one person understands and that person left eight months ago. Every hour your team spends doing incident triage on brittle automation is an hour they're not spending on the product roadmap. And it compounds. Technical debt piles up, release cycles slow down, and somehow the "temporary" fix from Q3 is still load-bearing in Q1.
Stakeholders want AI-driven workflows -- totally reasonable in 2026 -- but your team is already buried in sprint commitments
Risk: Evaluating frameworks, tuning prompts, building retrieval pipelines -- none of that fits between your Monday standups and Friday deployments. So the project sits in "exploratory" forever. Meanwhile, competitors who actually shipped working automation six months ago are pulling ahead on operational efficiency. And by the time your team surfaces from proof-of-concept mode, the gap is a lot harder to close.
Failed AI pilots are genuinely expensive, and not just in dollars
Risk: You build something, it looks great in the demo, stakeholders get excited -- and then it can't go to production because there's no audit trail, no retry logic, no guardrails against the model just confidently making things up. One bad pilot like that can poison the well for every future AI initiative. Executives remember. Budget conversations get harder. Engineering time gets redirected. That's the real cost nobody puts in the post-mortem.

How We Build This Right

Every safeguard, built in from Day 1.

Human-in-the-Loop Controls

Not every agent action should fire automatically. So every agent we build includes configurable approval gates -- meaning high-stakes actions like sending emails, modifying records, or triggering payments require explicit human sign-off before anything actually executes. You define the thresholds. The agent does the work, stops at the gate, and waits. Simple as that.

Deployed on Your Infrastructure

Your data stays yours. Agents run inside your cloud account or your on-premise environment -- no third-party SaaS sitting in the middle, holding your prompts, storing your query history, or creating a compliance headache your legal team has to untangle later. You stay inside your existing compliance perimeter. Honestly, for most enterprise clients we work with, this alone is the deciding factor.

Structured Audit Logging

Every single thing an agent does gets logged -- tool calls, decision steps, model responses, timestamps, input and output payloads, all of it. So when something goes wrong (and eventually something always does), you're not guessing. You pull the trace, see exactly what happened and why, and fix it. Same logs work for compliance review and ongoing performance monitoring. No black boxes.

What We Build

Purpose-built features for your industry.

Tool Calling and API Orchestration

Agents need tools to actually do things, and we build those tool definitions with proper typing so the model can't just invent parameters that don't exist. Each integration -- whether it's a REST API, GraphQL endpoint, internal database, or third-party service -- gets its own typed definition. The model then selects and sequences those tools based on what the task actually requires. No hardcoded if-else logic that breaks the moment a new edge case shows up.

RAG Pipeline Integration

Hallucination is a real problem, and RAG pipelines are the practical fix. We build retrieval-augmented generation setups that ground agent responses in your actual proprietary data -- your documentation, your support history, your structured product data, whatever's relevant. The model stops guessing and starts pulling from sources you control. In practice, this dramatically tightens output quality and keeps answers relevant to your specific domain instead of whatever the model learned during pretraining.

Multi-Step Workflow Execution

Real workflows aren't single-step. They involve gathering data from one place, transforming it, calling a downstream service, waiting for a response, handling errors, and returning something structured at the end. Our agents handle all of that -- plus retry logic when something fails and escalation paths when it fails too many times. So instead of a human babysitting each step, the agent runs the sequence and only surfaces problems that actually need human judgment.

Observability and Evaluation

You can't improve what you're not measuring. Every deployment we ship includes tracing instrumentation and an evaluation harness so you can track task success rates, latency, and model decision quality in production -- not just in staging, not just in your head. You get real numbers. And when something drifts, you'll see it in the dashboard before a user files a ticket about it.

Built on a Modern, Secure Stack

Next.jsVercelSupabaseOpenAIAnthropicLangChainVercel AI SDKPostgreSQLpgvector

Our Development Process

From discovery to launch. Quality at every step.

01

Workflow Audit and Scoping

1 week

Before we write a single line of code, we sit down with your team and actually map what's happening today. Which workflows are manual? Which are semi-automated but fragile? We identify the highest-value automation targets based on time saved and error rate -- not just what sounds cool. Then we define the agent's tool surface, data sources, and success criteria so everyone agrees on what "done" actually means before the build starts.

02

Architecture and Prompt Design

1-2 weeks

Model selection, tool schema design, system prompt architecture, few-shot examples -- all of that gets designed before integration code gets written. We also define the human-in-the-loop checkpoints and failure escalation paths up front, because retrofitting those in later is painful and expensive. Better to get the structure right while we're still in design mode.

03

Integration Build and Evaluation

2 weeks

This is where it all comes together. We build the agent runtime, connect every tool integration, wire up the RAG pipeline if the project calls for it, and then run a structured evaluation suite against real task samples -- not synthetic test cases we invented. Accuracy and reliability get validated before any production traffic touches the system. No surprises on launch day.

04

Production Deployment and Handoff

1 week

Deployment goes to your infrastructure. We configure observability dashboards, write up the system architecture and prompt management process, and run a knowledge transfer session with your team so they can actually extend and maintain the agent without needing to call us for every change. The goal is that you own it -- fully -- when we're done.

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Frequently Asked Questions

A chatbot responds to a message. That's it. An AI agent reasons about a task, decides which tools it needs, calls those tools -- APIs, databases, file systems, whatever's connected -- and executes a multi-step workflow on its own. We're talking booking appointments, processing refunds, generating reports, triggering real downstream actions. Not just replying with text and hoping a human picks it up from there.
We're model-agnostic, and in practice that matters a lot for cost. Most projects end up running a mix -- GPT-4o or Claude Sonnet for the complex reasoning steps, something lighter like GPT-4o-mini for simple classification or routing. Our architecture lets you assign different models to different workflow steps, so you're not paying frontier-model prices for tasks that don't need it. And if you need everything on-prem, we support self-hosted models via Ollama or vLLM.
Three layers, working together. Structured tool calling with typed schemas forces valid outputs -- the model can't just freestyle parameters. RAG pipelines keep responses grounded in your actual data instead of the model's best guess. And human-in-the-loop checkpoints catch edge cases before high-stakes actions execute. Plus, automated eval suites run before every deployment so accuracy regressions get caught before they hit production, not after.
Yes, and we've done it with some pretty gnarly stacks. Agents can connect to anything with an API -- CRMs like Salesforce, ERPs, internal databases, email platforms, Stripe, you name it. Each integration gets a typed tool definition with proper authentication, rate limiting, and error handling built in from the start. No API? We'll build a lightweight one, or bridge the gap with browser automation if that's what it takes.
A focused single-workflow agent typically runs 4-6 weeks from kickoff to production. Multi-agent systems with several tool integrations and layered approval workflows usually land in the 8-12 week range. Either way, every project includes a 30-day post-launch window -- because prompt tuning and performance optimization after you have real production data is just as important as the build itself.
For teams where data sensitivity is non-negotiable, we deploy entirely on your infrastructure. Regulated industries -- healthcare, finance, legal -- can run agents inside a VPC with nothing leaving the network. Private LLM endpoints, encryption at rest and in transit, full audit logs of every agent action and tool call. We've set this up in environments where even the model weights can't touch an external server. If your compliance team has a checklist, we've probably seen it.
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