AI Agents · production-grade

Custom AI agents that finish the job.

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.

Start a project See agents we shipped

Audit log on every call Schema-validated outputs Kill switch on day one

How we de-risk it Fixed quote · You own every repo & key · No lock-in You own every key and line of code

LLM providers supported

Schema-validated outputs (%)

Hardcoded API keys

Long-term key custody

How they work

Six capabilities, one agent.

Workflow execution, retrieval, voice and vision, tool-calling, persistent memory, and evals, composed into agents that finish jobs end-to-end.

Workflow

multi-step jobs

RAG

retrieval + citations

Voice

real-time STT + TTS

Vision

doc + image parse

Tools

tool-calling + APIs

Memory

persistent context

Four kinds of agents

If you can describe the work in a runbook, we can build it.

Different surfaces, same engineering muscle: structured outputs, schema validation, evals, and audit trails by default.

Workflow agents

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.

Tool-callingStructured outputAudit logSlack · Notion · GitHub

Multi-agent pipelines

Planner → workers → critic loops with shared memory and human-in-the-loop checkpoints for high-stakes steps.

LangGraphHITL

RAG over docs

Hybrid retrieval (BM25 + dense + reranker), source citations, freshness, ACL-aware filtering. RAG development service →

pgvectorReranker

Voice agents

Real-time STT + TTS (Whisper, Cartesia, OpenAI Realtime, LiveKit) for support and outbound calls.

WhisperCartesia

Vision agents

Document parsing, inspection, screenshot QA, automated visual review.

GPT-VisionClaude Vision

Evals + observability

Curated test sets, regression catches, prompt versioning, latency / cost dashboards.

BraintrustLangfuse

Sample tool-call

Structured, schema-validated.

agent.py · Claude tool use

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.

Common questions

Things people ask first.

What kinds of AI agents have you built?

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.

Which LLMs and frameworks do you work with?

Claude, GPT, Gemini, Llama, Mistral. LangGraph, LlamaIndex, Vercel AI SDK, plus custom orchestration. Vector DBs: pgvector, Pinecone, Weaviate, Qdrant.

How do you handle hallucinations and safety?

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.

Can you fine-tune or just prompt?

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.

Do you build voice agents?

Yes. Real-time voice with Whisper / Deepgram for STT, Cartesia / ElevenLabs for TTS, OpenAI Realtime or LiveKit Agents for orchestration.

"ShazraLabs exceeded our expectations. Their team moved with speed, professionalism, and a deep understanding of Web3 infrastructure, delivering a high-quality product without compromising performance or security."

Mario Butler, MBA

Founder, Dreamster

From the blog

AI agent field notes

Practical write-ups on AI agents, RAG, and agent security, from the team that ships them.

Cost11 min read

Have an AI workflow you'd rather not run by hand?

Send a one-paragraph description and we'll send back a build plan with a fixed price and a deployment date.

Start a project contact@shazralabs.com