- You have a specific, well-defined task that's currently done manually and is high-volume or time-consuming
- You want AI integrated with your existing systems, not a separate tool your team has to remember to use
- You've tried off-the-shelf AI tools and found them too generic for your actual use case
- You understand AI has limitations and want something reliable within those limits, not a silver bullet
- You want ongoing support after launch, not just a handoff
Service · 06 of 06
Built for production.
Not just demos.
Custom AI agents for customer support, internal workflows, and lead automation — integrated with your systems, tested properly, and supported long-term.
Why this matters
Most AI projects never make it out of the demo.
The gap between a compelling AI demo and something that works reliably in production is enormous. Most vendors sell you the demo. We build the production version — integrated with your actual systems, trained on your actual data, tested against your actual edge cases, and monitored after it goes live. AI that works is useful. AI that sometimes works is a liability.
What we build
Five types of AI agent work.
Customer Support Agents
AI agents that handle tier-1 support — answering questions, resolving common issues, escalating when needed. Trained on your documentation and policies, integrated with your helpdesk, and calibrated to hand off gracefully rather than frustrate users.
Internal Workflow Automation
Agents that handle repetitive internal tasks — data extraction, report generation, cross-system updates, approval routing. Work that currently takes someone an hour, done in seconds. Integrated with your existing tools rather than requiring you to replace them.
Lead Automation & CRM Integration
AI that qualifies leads, drafts outreach, updates CRM records, and triggers follow-up sequences — all based on what's actually in your pipeline. Less time on admin. More time on the deals that matter.
Knowledge & RAG Systems
Retrieval-augmented generation systems that let AI answer questions accurately from *your* internal knowledge base — documentation, past projects, policies, product specs. AI that knows your business, not just generic internet knowledge. Built with proper chunking, indexing, and retrieval so answers are actually correct.
Agent Infrastructure & Maintenance
The ongoing work that keeps AI agents reliable — monitoring output quality, updating prompts as your data changes, handling model updates, catching regressions. AI agents drift without maintenance. We keep them calibrated.
How we approach it
Scoped before it's
built.
( 01 )
Discovery & Scoping
We start by understanding the actual workflow — what triggers it, what data it touches, what a good outcome looks like, and where the edge cases are. Most AI projects fail because this step is skipped. We don't skip it.
( 02 )
Build & Integration
We build the agent and connect it to your actual systems — APIs, databases, helpdesks, CRMs. Not a standalone demo. A working component of your existing stack with proper authentication, error handling, and logging.
( 03 )
Testing & Calibration
We test against real inputs, including the messy ones. Prompt iteration, edge case handling, hallucination mitigation, and output validation before anything goes live. AI in production needs to be right most of the time, not just sometimes.
( 04 )
Deployment & Monitoring
Live deployment with monitoring in place from day one. Output quality tracking, error alerting, and regular calibration as usage patterns evolve. AI agents need ongoing attention — we provide it.
Right fit
This is for you if...
You'll get value
from this if:
This probably isn't
a fit if:
- You want a demo to show investors with no plan to put it in production
- You want to fully replace human judgement in a high-stakes, unstructured domain
- The underlying workflow is too undefined or inconsistent for automation to work reliably
- You're not willing to provide the data access needed to actually integrate with your systems
What to expect
Honest about what AI can and can't do.
- Typical timeline: 4–8 weeks from scoping to live deployment
- Engagement type: Project-based build + optional ongoing retainer
- What we need from you: Access to relevant systems, examples of real inputs and expected outputs, and someone who knows the workflow
- What AI is good at: High-volume, structured, well-defined tasks with clear success criteria
- What AI isn't good at: Ambiguous judgement calls, novel situations, anything where being wrong has serious consequences
- Common outcomes: Hours saved per week on manual tasks, faster response times, consistent output quality, and staff focused on work that actually needs a human
What it connects to
AI works best when
the platform is solid.
Enterprise Web & Application Development
The platform the agent lives inside.
Cloud Infrastructure & DevOps
AI agents need reliable infrastructure to run on.
Managed Platform & Growth Operations
Ongoing support keeps agents calibrated over time.
Common questions
What people ask
before building.
It depends on the task. We're model-agnostic — we use whatever performs best for the specific job, whether that's GPT-4o, Claude, Gemini, or a smaller open-source model. Cost, latency, and accuracy all factor into the choice. We don't have a vendor relationship that biases our recommendations.
We design for it from the start. RAG systems, structured outputs, validation layers, human-in-the-loop checkpoints for high-stakes decisions, and output monitoring post-launch. The goal is a system that's reliable within a defined scope — not one that tries to do everything and occasionally gets it catastrophically wrong.
No. We use your data to build and tune the system, but it stays yours. We're careful about how data flows through third-party APIs, and we'll be transparent about which providers handle what so you can make informed decisions about data residency and privacy.
Model updates can break things — a prompt that worked perfectly on one version behaves differently on the next. We monitor for this and treat model updates like dependency updates: tested before they go live, not just rolled forward blindly. This is part of why ongoing support matters for AI systems.
Not always. For RAG systems, you need your existing documentation and knowledge base — you probably already have this. For workflow automation, we need examples of real inputs and the expected outputs. For fine-tuning, larger datasets help, but many use cases don't require fine-tuning at all. We'll tell you honestly what you actually need.