What we build
Customer facing action agents
Agents that don't just answer — they issue the refund, change the order, and book the appointment, then escalate with full context when a human is genuinely needed.
Back office & operations agents
Reconciliation, data entry, and status chasing handled end to end — the repetitive work your team does between systems, done in seconds instead of hours.
Document processing agents
Ingest contracts, statements, and forms, extract what matters, and produce a decision you can defend — with every output traced back to its source document.
Lead & sales agents
Every inbound lead engaged, enriched, qualified against your criteria, and booked into your CRM — before a competitor has even replied.
Internal copilots that take action
Knowledge agents grounded in your data (RAG) that don't stop at answers — they update the ticket, draft the reply, and trigger the workflow on your behalf.
Multi step workflow orchestration
Agents that plan across several tools and APIs, call each one in the right order, check their own work, and only ask a human when the stakes call for it.
See how it works in action
What an AI agent does for you
Takes action, not just answers
A chatbot replies; an agent completes the task — refunds issued, records updated, tickets closed — so work gets done, not deflected to a queue.
Works across your whole stack
It reads from and writes to the tools you already run — CRM, helpdesk, ERP, calendars, internal APIs — instead of trapping the work in one more silo.
Runs 24/7 at scale
One agent handles the same volume as a room of people, at 2am and at month end peak alike, without the queue or the overtime.
Every decision is traceable
Each action references the data and policy it acted on, so you get a regulator grade audit trail instead of a black box you have to trust blindly.
Escalates with full context
When something falls outside its guardrails, it hands off to a human with the whole thread and its reasoning attached — no starting from zero.
Gets sharper over time
Every resolved case and correction feeds evaluation, so accuracy and coverage climb sprint over sprint against the KPI you set at kickoff.
What you get
- Manual work eliminated — the repetitive majority of a process automated, like the 80% we removed from background checks for Future Mortgage.
- Turnaround cut from hours or days to minutes, on the workflows that currently bottleneck your team.
- A regulator grade audit trail — every decision traceable to the source data and policy it acted on.
- Full ownership — code, prompts, models, and documentation are yours. No per seat subscription, no vendor lock-in.
- Numbers you can show the board — monitoring, a /status page, and the KPI line we agreed on at kickoff moving in the right direction.
Industries we've shipped in
Engagements that fit the stage you're in
Discovery call
A clear read on whether an AI agent would pay back in your workflow, which process to start with, and what it would cost.
Ideal for:
- A live business with a repetitive, high volume process to automate
- Non-technical founders or executives who need tech validation
- Teams comparing AI agent development companies before committing
Agentic system PoC
A working agentic system on your data, integrated with your tools — live demo within the first 5 days, validated against your real workflow.
Ideal for:
- You want an agent taking real action but need proof on your data first
- An off the shelf or no code tool broke on your real workflow
- You need accuracy and containment numbers to greenlight the build
Deliverables:
- Working agentic system on your data
- Live demo within 5 days
- Accuracy and autonomy benchmarks
- Integration and architecture plan
- MVP scope and budget you can take anywhere
Frequently asked questions
A chatbot answers questions; an AI agent takes action to complete the task. Agents plan across steps, call your tools and APIs, and actually book, refund, verify, or escalate — a chatbot mostly deflects. We break down when each one fits in AI Agent vs Chatbot.
Through the same APIs and integrations your team already uses. We connect agents to your CRM, helpdesk, ERP, databases, calendars, and internal services — via native integrations, REST APIs, or protocols like MCP — with scoped, least privilege access. The agent reads from and writes to your systems of record, so nothing lands in a silo.
Yes, when they're built with the right guardrails — and that's how we build them. Agents run with scoped permissions, policy checks before high stakes actions, human in the loop approval where you want it, and a full audit trail for every decision. You decide exactly which actions an agent can take on its own and which need a human sign off.
It depends on scope. A validated proof of concept on your own data runs $8k–35k over 4–8 weeks; a full production build is quoted per scope after that. We break down exactly where the money goes in our guide, How Much Does It Cost to Build an AI Agent in 2026.
AI agent development is building software that uses large language models to plan and take actions toward a goal — calling tools, APIs, and databases, checking its own work, and escalating when needed. Unlike a chatbot that only responds, an agent is developed to complete multi step tasks inside your systems, grounded in your data and bounded by guardrails you control.
The ones that pay back fastest are document processing, back office automation, support resolution, lead qualification, and internal copilots that take action. We ranked nine of them with real examples, honest costs, and ROI in Top 9 AI Agent Use Cases for Business in 2026.
You see a working demo on your own data within the first 5 days. A validated proof of concept takes 4–8 weeks, and most production rollouts follow within a few two week sprints, depending on how many tools the agent integrates with and how autonomous you want it to be.
Let's scope your AI agent
Book a 30-minute call. We'll map your workflow, tools, and data, and tell you honestly whether an AI agent moves your numbers.