Workflow agents, multi-agent pipelines, RAG over your private docs, and voice / vision agents, all built with tool calls, memory, audit trails, and a kill switch. From prototype to production in weeks, not quarters.
Workflow execution, retrieval, voice and vision, tool-calling, persistent memory, and evals, composed into agents that finish jobs end-to-end.
Different surfaces, same engineering muscle: structured outputs, schema validation, evals, and audit trails by default.
Single-purpose agents that finish a multi-step job: ticket triage, lead enrichment, invoice processing, data entry, content QA. Tool-calling against your stack (Slack, Notion, Salesforce, Linear, GitHub) with structured outputs and an audit log.
Planner → workers → critic loops with shared memory and human-in-the-loop checkpoints for high-stakes steps.
Hybrid retrieval (BM25 + dense + reranker), source citations, freshness, ACL-aware filtering. RAG development service →
Real-time STT + TTS (Whisper, Cartesia, OpenAI Realtime, LiveKit) for support and outbound calls.
Document parsing, inspection, screenshot QA, automated visual review.
Curated test sets, regression catches, prompt versioning, latency / cost dashboards.
tool = {
"name": "create_linear_issue",
"input_schema": {
"type": "object",
"properties": {
"title": {"type": "string", "maxLength": 120},
"priority": {"type": "integer", "enum": [1, 2, 3, 4]},
"assignee": {"type": "string"}
},
"required": ["title", "priority"]
}
}
# Every call audit-logged. Failed schema = retry with feedback.
Workflow agents (ticket triage, data entry, lead enrichment), multi-agent pipelines for research and content, RAG over private docs, and voice / vision agents for support and inspection. Every one ships with tool calls, persistent memory, audit trails, and a kill switch.
Claude, GPT, Gemini, Llama, Mistral. LangGraph, LlamaIndex, Vercel AI SDK, plus custom orchestration. Vector DBs: pgvector, Pinecone, Weaviate, Qdrant.
Retrieval grounding, structured outputs with schema validation, tool-call confirmation for any irreversible action, evals against a curated test set, and an audit log for every decision. Kill switches for anything touching money or production data.
Both. Most production agents work fine with strong prompts + RAG + tool use. We move to fine-tuning when latency, cost, or domain-specific behavior demands it. That usually means Llama / Mistral on a curated dataset.
Yes. Real-time voice with Whisper / Deepgram for STT, Cartesia / ElevenLabs for TTS, OpenAI Realtime or LiveKit Agents for orchestration.
What it costs to build an AI agent or chatbot in 2026: the models, RAG, tools, guardrails, and infra that move the number.
Read the guideThe 7-point checklist we run before shipping any agent: prompt injection, exposed MCP servers, and the real attack surface.
Read the guideScoped session keys, spend limits, allowlists, and human gates so a bad prompt can't drain an agent's wallet.
Read the guideThe hardening we run before connecting an agent to any tool server: auth, least privilege, and sandboxing.
Read the guideReal reply within a day, from someone who'll be writing the code. No sales desk in between.
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