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Quick answer
AI Agent Development Services
UnlockLive IT designs and ships production AI agents for North American businesses — the kind that actually deploy, not the kind that stop at a flashy demo. Our agents handle customer support, document processing, sales qualification, internal knowledge retrieval, browser automation, and voice interactions. We build with LangChain, LangGraph, the OpenAI Agents SDK, and Anthropic Claude, on top of FastAPI or Next.js backends. Every agent we ship includes retrieval-augmented generation where appropriate, a tool-use layer with proper authentication, structured evaluation tests, observability, and guardrails against prompt injection and hallucination.
What we build
Customer-facing AI assistants:Production chatbots and copilots embedded in SaaS products, web apps, and mobile apps. Streaming responses, conversation memory, tool use, escalation to human, full audit trail.
Internal RAG (retrieval-augmented generation) systems:Search-and-answer systems on top of your company's documentation, tickets, contracts, or knowledge base. Pinecone, Weaviate, Qdrant, or pgvector for retrieval; LLM of your choice for generation.
Multi-agent workflows:Agents that plan, decompose tasks, call tools, and hand off to other agents. Built with LangGraph, OpenAI Agents SDK, or AutoGen depending on the use case.
AI document processing:Invoice extraction, contract review, resume parsing, form intake — combining OCR (AWS Textract, Google Document AI), structured extraction (LLM with JSON-mode or function calling), and human-in-the-loop validation.
Voice agents:Real-time voice agents using Vapi, Retell, LiveKit, or custom pipelines on top of OpenAI Realtime API and Eleven Labs. Used for inbound support, outbound sales qualification, and appointment booking.
Browser-using and computer-using agents:Agents that operate browsers (Playwright + LLM) or full desktops (Anthropic computer-use, OpenAI Operator) for back-office automation, QA testing, and data entry.
Our AI agent technology stack
Our AI agent development process
- Discovery and use case design (1-2 weeks): We start by mapping the actual workflow you want to automate. Many AI projects fail because they were scoped as 'we need an AI assistant' instead of 'we need to reduce our 12-minute average handle time on tier-1 support tickets by 40%.' We help you define the success metric first.
- Prototype and model selection (1-2 weeks): Build a working prototype with 2-3 candidate model and architecture combinations. Run them against a representative dataset of 50-200 real examples from your domain. Pick a winner based on quality, latency, and cost.
- Production build (4-12 weeks): Full implementation with retrieval pipeline, evaluation suite, observability, fallback handling, rate limiting, prompt-injection defense, and deployment pipeline.
- Eval suite and red-teaming (1-2 weeks, parallel): Build an automated eval suite that catches regressions before they ship. Conduct prompt-injection and jailbreak testing. Document acceptable failure modes.
- Soft launch and iteration (2-4 weeks): Roll out to a small group of internal users or beta customers. Collect logs, sample failures, and tune prompts, retrieval, and routing based on real usage.
- Production rollout and ongoing optimization: Full launch with on-call coverage, weekly metric reviews, and a backlog of model and prompt experiments.
Frequently asked questions
What is an AI agent and how is it different from a chatbot?
A chatbot replies to messages. An AI agent uses an LLM to decide what actions to take, calls external tools or APIs, observes the results, and iterates until it has completed a multi-step task. A support chatbot might answer 'where is my order?' from a knowledge base. A support agent would actually call your order management API, look up the order, check the shipping carrier API, and write a response with the live tracking link — all without a human in the loop.
Which LLM should we use — OpenAI, Claude, or open source?
It depends on the use case. As of 2025, Claude (Sonnet 4.5 and Opus 4.5) leads on coding, agentic tool use, and long-context reasoning. OpenAI's GPT-5 family leads on general reasoning, multimodal, and the broadest tool ecosystem. Open-source models hosted on Groq, Together, or your own infrastructure are competitive on price and latency for narrower tasks. We routinely use a mix — for example, Claude Sonnet for the main agent loop and a smaller open-source model for cheap classification or routing decisions.
How much does it cost to build an AI agent?
A focused single-task agent (one workflow, one data source, one channel) typically ranges from $25,000 to $75,000 to build. A more capable multi-agent system with multiple integrations, RAG, evals, and a custom UI ranges from $80,000 to $250,000. Enterprise deployments with strict compliance, multi-tenant data isolation, and 99.9% SLAs start at $200,000. Ongoing inference costs are separate and depend on your volume and model choice.
How do you prevent hallucinations and prompt injection?
Hallucinations: ground every answer in retrieved sources, require citations, run automated factuality evals, and add a 'I do not know' fallback path. Prompt injection: structured tool inputs (no string concatenation into system prompts), allowlisted tool calls, output classifiers, separate context windows for untrusted data, and human-in-the-loop for high-risk actions. Every system we ship includes a documented threat model.
Can the agent integrate with our existing systems (Salesforce, Zendesk, internal APIs)?
Yes. Tool integrations are the core of agent development. We have built integrations with Salesforce, HubSpot, Zendesk, Intercom, Notion, Slack, Microsoft 365, Google Workspace, Stripe, AWS services, internal REST/GraphQL APIs, and SQL databases. Authentication is typically OAuth 2.0 or service accounts with least-privilege scopes.
Will our data be used to train models?
Not unless you explicitly opt in. OpenAI, Anthropic, Google, and AWS Bedrock all offer enterprise tiers with zero data retention and contractual guarantees that your data is not used for training. We default to those tiers and can deploy entirely on your own AWS, Azure, or GCP account if data residency is a hard requirement.
How do you measure if the agent is actually working?
Every agent we ship comes with three layers of measurement: (1) automated eval suite that runs on every prompt or model change, (2) production telemetry covering latency, cost, tool-call success rate, fallback rate, and user feedback signals, and (3) periodic human review of sampled conversations. We tie these to the business metric you defined in discovery — for example, ticket deflection rate, lead qualification accuracy, or hours saved per week.
Ready to build an AI agent that actually ships?
Tell us the workflow you want to automate. We will respond within one business day with a candid take on what is achievable, what it will cost, and how long it will take. Book a free strategy call with our Toronto team.
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