AI agent development company shipping production agents.

An AI agent development company designs and ships production agentic AI systems — multi-step LLM workflows that call tools, hold memory, route across services, and escalate to humans when confidence drops. The work spans agent architecture, tool integration, evaluation harnesses, guardrails (prompt-injection defence, PII redaction, jailbreak detection), and observability — not just prompt engineering.

A chatbot answers questions from a knowledge base. An AI agent acts — it calls tools (booking, billing, CRM writes), routes across services, holds memory across turns, and decides when to escalate. Agents need bounded recursion, typed tool whitelists, and continuous evals because the action surface is wider and the failure modes are more expensive than 'just a wrong answer'.

Six weeks from kickoff to a working v1 is the target. Week 1 scopes the eval set. Weeks 2–4 build the agent with typed tool calls and guardrails. Week 5 is hardening + red-teaming. Week 6 ships to real users. Pilots ship in 10 business days. Fine-tuned agents on custom data take 10–12 weeks.

Four layers. Retrieval grounding with required citations stops fabrication. Explicit tool whitelists stop the agent from inventing actions. Refusal layers reject out-of-scope queries. An eval harness blocks any prompt or model change that regresses hallucination rate, refusal accuracy, or tool-call success against the golden set. Every model and tool call is audit-logged for forensic review.

Most agent v1 engagements at Aiinfox land between $35,000 and $150,000 fixed-price depending on tool-integration complexity, compliance scope (HIPAA, SOC 2), and whether the agent is voice, text, or multimodal. Pricing arrives in writing within 72 hours of the discovery call — no timesheets, no scope-creep invoices.

Yes. We deploy to your AWS, Azure, or GCP VPC, to on-prem hardware for regulated workloads, or to our managed cloud. Self-hosted Llama 3 on vLLM is supported for zero-egress environments. Regional data residency (India, EU, US) is configurable per deployment.

Model-agnostic. We benchmark per task and pick the cheapest model that clears the eval bar — usually Claude Sonnet, GPT-4o, or self-hosted Llama 3. Orchestration via LangGraph, LlamaIndex, or custom Python. Voice stacks on Twilio, LiveKit, Vapi, Deepgram, ElevenLabs. Eval and observability via Braintrust, Langfuse, OpenTelemetry.

Yes. LoRA fine-tunes on open-weight models, full fine-tunes when warranted, or distillation to smaller models when latency or cost matters more than the last 2% of quality. Fine-tuning pipelines are reproducible with versioned data and weights, and re-runnable on schedule when your domain drifts.