Conversational copilots
Text agents grounded in your product, your data, and your tone of voice — with citations, not vibes.
RAG · citations · toneAI / ML · 01 of 05
Agents that do the work. Not just talk about it.
Goal-oriented AI agents and conversational copilots that pick up the ticket, call the right tool, stay inside your policy — and finish the job. From customer support deflection to internal ops automation, we ship agents that have a job description, not a demo script.
62%Avg. ticket deflection
4 wkPOC → production
< 800msp95 first-token
SESSION · #4812 · 6 tools
UserI can't find my October invoice — can you pull it and email a copy?
Tool call · billing.lookup
{ user: "u_4128", month: "2026-10" }Agent · planningFound invoice INV-0921. Verifying recipient, then sending PDF + Stripe receipt.
AssistantSent — you'll see INV-0921 in your inbox in <30s.
What you get
A complete agent stack — not a wrapper around an LLM.
Six things every production agent needs. We ship all six, instrument all six, and own them on call.
Voice & phone agents
Low-latency speech agents (Whisper / Deepgram / 11Labs) that handle inbound calls, qualify leads, and warm-transfer to a human.
< 800ms p95Multi-agent orchestration
Planner + worker + critic patterns with LangGraph or DSPy. Each agent has a narrow job — the supervisor handles the chaos.
LangGraph · DSPyTool & function calling
Typed actions on your real systems — Salesforce, Jira, your warehouse, your APIs. Strict schemas, dry-run mode, audit log.
strict schema · auditMemory & long context
Short-term, episodic and semantic memory layers — the agent remembers what matters and forgets what shouldn't stick.
summary · vector · TTLGuardrails & evaluation
Refusal policies, PII scrubbing, role-based access — plus an eval harness so quality is a number, not a hunch.
policy · eval · red-teamHow it works
Plan, act, observe, repeat — safely.
A reference agent loop we’ve hardened across 40+ deployments. Every box is replaceable, every edge is auditable.
InputUser · Voice · API
PolicyAuth · Scopes · PII
MemoryVector · Summary
ToolsCRM · Billing · Docs
EvalScore · Trace
ActionReply · Write · Call
Agent loopPlan → Act → Observe
The same loop every senior engineer would build — just shipped on day one.
Most teams spend three months reinventing this. We bring it on the bus, you spend that time on the parts that are actually unique to your business.
- 01
Plan with structure, not freeform
Typed plans (Pydantic / Zod) the model fills in, never a single “think step by step” prompt.
- 02
Act on real systems, with dry-run
Every tool call is shadow-executed first in staging — you preview impact before it touches prod.
- 03
Observe with traces, not just logs
Every run replayable in LangSmith / Braintrust. One-click diff between a good and a broken trace.
- 04
Improve from production data
Failures become labeled examples. Eval suite grows. Quality is a number that goes up week over week.
Tech stack
A toolbox tuned for agents — not generic ML.
Every chip below has paid rent in a production deployment we operate. No buzzwords on the shelf.
Orchestration
LangGraphCrewAIAutoGenDSPyTemporal
Models
Claude 3.5GPT-4oGemini 1.5Llama 3.1Mistral
Voice
WhisperDeepgramElevenLabsOpenAI RealtimeLiveKitTwilio
Retrieval
pgvectorWeaviatePineconeQdrantElasticsearch
Eval & Obs
LangSmithBraintrustHeliconePhoenix
Guardrails
NeMo GuardrailsGuardrails.aiRebuffPresidio (PII)
Integrations
SalesforceHubSpotZendeskSlackJira
Runtime
FastAPINode.jsvLLMModalCloudflare Workers
From vision to victory
A four-week path to your first agent in production.
Same five-step rhythm whether it’s a support copilot or an outbound voice agent. No phase-zero theater.
01
Week 1Define
Pick one task, agree the success metric in writing, identify the 3 tools the agent will call.
02
Week 1–2Wire tools
Typed function specs, dry-run mode, sandbox auth. The agent can’t move money until you say so.
03
Week 2–3Train & eval
Prompt + retrieval + policy. 50-case eval harness from your actual transcripts.
04
Week 3–4Ship behind a flag
Canary 5% of traffic. Human-in-the-loop review queue. Compare against control.
05
OngoingOperate
Weekly eval review, drift watch, monthly retraining on labeled failures.
Where agents earn their keep
Three patterns that pay back fastest.
If your problem rhymes with one of these, we’ve got a head start — including an evaluation set, a baseline agent, and the failure modes we’ve already mapped.
Deflect & assist, never disappoint.
Tiered agent: deflects FAQs end-to-end, drafts replies for L1, summarizes for L2. Always offers a human, never gates the human behind a maze.
62%Tickets deflected
−47%AHT for L1
+18 NPSvs. previous bot
Claude 3.5LangGraphpgvectorZendesk
The 4pm-Friday assistant.
RevOps copilot that pulls Salesforce + warehouse + Notion to answer questions like “why did EMEA pipeline slip this quarter?”
3.4×Faster than analyst
92%Cited answers
GPT-4oDSPySnowflakeSlack
A phone agent that hangs up nicely.
Inbound voice agent for a clinic network: triage, scheduling, refill requests. Warm-transfers to a human within 800ms when needed.
71%Calls fully handled
HIPAAAudit-ready
OpenAI RealtimeLiveKitTwilio
Why ETY
Senior agent engineers. On the hook.
22+Agents live in production today across 9 clients.
4 wkMedian time from kickoff to a working agent on canary traffic.
0P0 incidents caused by an agent in the last 12 months — guardrails work.
24×7On-call coverage for systems we operate end-to-end.
One agent. One task. Four weeks.
Tell us the one task that eats your team’s week. We’ll come back with a scoped four-week build or, honestly, the reason it isn’t agent-shaped yet.