Agents that hold
context, call tools,
and know when to ask.
For the work where a single prompt isn't enough. We build bespoke AI agents engineered like software — with tool calling, memory, escalation, observability, and continuous evaluation. Not chatbots. Production agents that hold up at scale.
Agent roles we've shipped
Sales SDR Agent
Researches a lead across the web, the CRM, and your past conversations. Drafts a personalized first-touch email, handles routine replies, and only escalates to a human when the deal is real. Reps get a triaged pipeline, not a queue.
Support Tier-1 Agent
Reads incoming tickets with full conversation history, drafts an answer using your knowledge base, and resolves the easy 60% autonomously. Hard cases get escalated with a clean handoff and full context — no "please describe your issue again."
Ops Coordinator Agent
Pulls data across your CRM, project tool, finance system, and Slack. Synthesizes status updates, drafts customer-facing summaries, flags exceptions to humans. The agent that finally makes "what's the status of X?" answerable in under 30 seconds.
Finance Close Agent
Reconciles transactions across systems, flags variance and anomalies, drafts narrative commentary for monthly reviews, prepares board-ready summaries. Your CFO becomes 3x faster without hiring.
Lead Qualification Agent
Scores inbound demo requests, runs deep enrichment, prepares a pre-meeting brief for the rep. Reps walk into every call already knowing the company, the role, the likely use case, and the budget signals.
Procurement / Vendor Agent
Reads supplier emails, reconciles POs against deliveries, drafts replies, flags exceptions. The agent that absorbs the back-and-forth nobody on your team wants to do.
What separates an agent that works
from a chatbot that doesn't.
Tool Calling
Agents call your real systems — your CRM, your billing, your knowledge base, your custom internal API. Not a sandbox demo. Real reads and writes, with proper auth and audit trails.
Memory & Context
Agents remember the conversation, the customer, and the history. They're not generic chatbots resetting after every reply — they hold context the way a competent human team member would.
Escalation
When the agent doesn't know, it knows it doesn't know. Edge cases route to a human with full context attached. No false confidence, no wrong answers shipped to customers.
Observability
Every agent decision is logged. Every tool call is traced. Every cost is tracked. When you ask "why did it do that?", we can show you — line by line.
Evaluation
We don't guess if an agent is good. We test it against real historical data — emails, tickets, deals — and measure accuracy before it goes live. Then we measure again every month.
Cost Control
Agents can run away with model costs if you're not careful. We engineer cost ceilings, smart routing between cheap and expensive models, and per-execution budgets so you never get a surprise bill.
When you need an agent, not a workflow
A workflow alone isn't enough.
You've tried single-prompt automations and hit a ceiling. The work needs context, tool access, and judgment that linear workflows can't provide.
You want to replace a function, not a task.
You're thinking "what if our SDR/support tier-1/ops coordinator function was 10x cheaper?" — not "can AI write this one email?"
You care about quality at scale.
Volume is killing your team. You need consistent execution across thousands of cases, not a batch of inconsistent half-answers.
You want to know it'll keep working.
You've seen the demos. You want the version with logging, monitoring, evaluation, and a human paid to make sure it doesn't silently degrade.
How we build agents you can trust
Role Definition
We define exactly what the agent does, what it has access to, and where it escalates. Inputs, outputs, edge cases, and what "good" looks like — measurable.
Build & Evaluate
We build the agent against real historical data. Before any human ever sees its output, it's been tested on hundreds of past cases. We measure accuracy, escalation rate, and cost-per-decision.
Soft Launch
Agent runs alongside humans for 2–4 weeks. Every output is reviewed before being acted on. Gaps get tuned. Edge cases get added to the eval suite. Trust gets earned, not assumed.
Production
Agent runs autonomously on the work it's proven to handle. Hard cases continue to escalate. Monthly evals run automatically. Nothing silently degrades on your watch.
Agent build questions
A workflow is a defined sequence of steps. An agent decides what steps to take based on the situation. Workflows are great for predictable, linear work; agents are necessary when the work needs judgment — choosing which tool to call, when to escalate, what to say to whom.
Almost never the goal. Most clients use agents to handle the repeatable 60–70% of a function, freeing the human team for the harder 30–40% that genuinely needs human judgment. Net effect is more output without more headcount, not the same output with fewer humans.
Three layers: (1) tool access is scoped — the agent can only call the systems and actions you authorize; (2) every decision is logged, so unusual behavior surfaces fast; (3) evals run continuously against ground truth so accuracy doesn't silently drift.
Whatever fits the task. We mix Anthropic Claude, OpenAI GPT, and open-weights models depending on accuracy needs, cost profile, and data sensitivity. Routing between models is part of the engineering — cheap models for easy cases, expensive ones only when needed.
Typically 4–8 weeks from kickoff to production. The "soft launch" period — where the agent runs alongside a human — is what makes the difference between an agent you trust and one you suspect.
Yes. We work with SOC 2-compliant model providers, handle PII redaction where needed, support self-hosted deployments for sensitive data, and document data flows for compliance review. Healthcare, finance, and legal clients have specific patterns we can replicate.
Other ways we can help
Workflow Audit & Roadmap
Two weeks. A ranked backlog of every automation worth building.
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