AI Agents & Automation
Custom AI agents, workflow automations, and scalable database backends built as proprietary assets that scale alongside your company's growing operational requirements.
- Custom AI agents for sales, support, and operations
- Bespoke software backends & API integrations that grow with you
- Human-in-the-loop review flows and dashboards
Software built for scale. AI engineered to work.
We build secure, robust software engines and autonomous AI agents designed to grow and scale natively with your company's transaction requirements.
"sender": "steve@scalecorp.com"
"subject": "Request to upgrade corporate tier SLA"
"body": "Hi Team, we recently crossed 50 employees and want to scale our custom database synchronization pipeline..."
AI Agents & Automation that earns its keep.
We build custom AI agents, automated workflow pipelines, and operational systems that act as dedicated business assets. Rather than fragile, off-the-shelf templates, we engineer bespoke database structures, custom API endpoints, and scalable software pipelines designed to adapt and expand as your organization's compliance, workload, and team size scale.
What we cover, end to end.
Custom AI agents, workflow automations, and scalable database backends built as proprietary assets that scale alongside your company's growing operational requirements.
- 01 / 06
Custom AI agents
Sales, support, and ops agents built on Claude, GPT-4, and open models - with tool use, memory, and the right guardrails.
- 02 / 06
Scalable Software Backends
Bespoke database nodes, custom APIs, and backend pipelines engineered in Go, Python, or Node.js that anchor your AI workflows and scale natively with your transaction volume.
- 03 / 06
Workflow automation
n8n, Make, Zapier, and custom code to wire your CRM, helpdesk, sheets, and product into one operating system.
- 04 / 06
RAG & knowledge bases
Retrieval over your docs, tickets, and product so the agent answers from your truth - not the model's hallucinations.
- 05 / 06
Voice & chat agents
Inbound and outbound voice, web chat, and WhatsApp agents for support, qualification, and recovery flows.
- 06 / 06
Evals & observability
Every agent ships with logging, evals, and a dashboard so you can see exactly what it did - and catch regressions before users do.
What you actually get.
Every engagement ships these as standard. Anything outside this list gets scoped explicitly so there are no surprise add-ons.
- Mapped automation opportunities and ROI estimate
- Production agent or workflow in your stack
- Scalable codebase and database infrastructure handed over to your team
- Eval suite and human review queue
- Logging, monitoring, and alerting dashboard
- Runbook for owners and operators
- Optional retainer for tuning and new flows
A process built for shipping.
- Step 01
Map
We sit with your operators, watch the work, and rank automations by hours saved per week vs build complexity.
- Step 02
Prototype
Working prototype within 1-2 weeks, tested against real inputs from your team. We kill ideas that don't earn their cost.
- Step 03
Harden
Evals, guardrails, retries, fallbacks, and a human review step where the cost of being wrong is high.
- Step 04
Operate
Deployed with observability, on-call alerts, and weekly drift checks. We tune the agent until it's boring and reliable.
Common questions.
The questions every founder asks on the first call - answered up front.
Where does my data go?
We default to enterprise-grade APIs (Anthropic, OpenAI) with zero data retention, or self-hosted open models when compliance demands it. Your data does not get used to train models.
Which model do you use?
Whichever wins the eval. We benchmark Claude, GPT-4 class models, and open-source options on your task before we commit.
How does your software adapt as our company requirements grow?
Unlike closed SaaS platforms or rigid drag-and-drop templates, we write clean, proprietary code on open-source standards. We design database schemas, queue architectures (like Redis/BullMQ), and API structures with clear isolation, meaning they can easily be expanded, split into microservices, or migrated as your transaction volume and security requirements scale.
How do you measure ROI?
We baseline the manual hours and error rate before launch, then track the same metrics weekly. Most agents pay back inside one quarter.
When should we NOT use AI?
When the workflow is high-stakes and rare, or when a 50-line script does the job. We'll tell you - sometimes the answer is automation without an LLM.
Do you offer ongoing support?
Yes. Most clients keep us on a small retainer to monitor evals, ship new flows, and adapt as model providers update their APIs.
Ready to talk ai agents & automation?
30 minutes with a senior operator. We'll pressure-test your goals, share what we'd do, and tell you honestly if we're the right fit.