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.

By task shape, not by trend. A single-agent ReAct loop covers most support, booking, and document-intelligence tasks and is the easiest to evaluate and debug, so it is the default. A planner-executor split earns its complexity when a task has enough sequential steps that one model loses coherence tracking them. A multi-agent swarm earns its complexity when sub-tasks are genuinely independent — inventory, marketing, and customer service each with their own tool whitelist — and the coordination overhead does not outweigh the parallelism gained. We benchmark the simplest architecture against your eval set first and only add complexity when the simple version demonstrably fails.

Two different things get called "memory." Short-term session memory — holding context across the turns of a single conversation — is table stakes, and every agent we build has it. Long-term memory — recalling something from a prior session weeks later, usually via a vector store — is a real engineering cost that is worth paying only when the task genuinely needs it, like a support agent that should remember a customer's prior ticket history. We do not add a vector memory store by default; we add it when the eval set shows the agent fails without it.

Bounded recursion with a hard step ceiling, explicit tool whitelists so the agent cannot invent an action outside its allowlist, and a refusal layer that fires when a tool call fails repeatedly or confidence drops below threshold rather than letting the agent keep retrying. Every agent we ship has a maximum-steps cutoff that hands off to a human rather than looping silently — a production agent that never explicitly fails is usually one nobody has stress-tested.

Yes. Self-hosted Llama 3 on vLLM inside your AWS, Azure, or GCP VPC covers the model layer; your existing Postgres with pgvector or a self-hosted Qdrant instance covers retrieval; and the whole agent — reasoning, tool calls, memory, logging — runs with zero egress to a third-party endpoint. This is the default pattern for clients whose compliance posture (HIPAA, SOC 2, defence-adjacent) rules out any external API touching production data.