Production AI Agents That Do Real Work, Not Just Chat
Expedey is an agentic AI engineering company with offices in Houston, Texas and Karachi, Pakistan. We build production AI systems: AI agents, the harnesses that make them reliable, multi-agent workflows and RAG, plus the custom software they run in.
We design, build and run AI agents that read documents, call your APIs, make decisions inside clear limits and hand off to a person when they should. Then we engineer the harness around them, with evaluations, guardrails, logging and cost controls from day one.
AI Agents We Build
An AI agent is software that uses a large language model to plan steps, call tools such as APIs and databases, and complete a task with limited supervision. These are the agents we build most often.
Document and back-office agents
Agents that read contracts, RFPs, invoices and scanned PDFs, extract what matters, check it against your rules and draft the next document.
Customer support agents
Agents that answer from your help center and order data, take safe actions such as status lookups, and escalate to a person with the full context.
Sales and operations agents
Agents that qualify leads, update the CRM, prepare follow-ups and draft proposals from your past work.
RAG knowledge assistants
Search-and-answer over your internal documents, with source citations, per-user access control and automatic re-indexing.
Multi-agent workflows
Several specialised agents coordinated by an orchestrator such as LangGraph, with shared state, retries and checkpoints.
AI features and AI-powered MVPs
LLM features added to an existing SaaS product, or a new AI-powered MVP taken from prototype to launch.
Harness Engineering: How We Make AI Agents Production-Ready
Most agent demos break on real data. Harness engineering is the software around the model, from the agent loop and tools to context, guardrails, evaluations and monitoring, that decides whether an agent can be trusted with real work.
Real tool calling and integrations
Typed tool definitions, validated inputs and safe retries for every API the agent can call. We test against sandbox accounts before touching live systems.
Permissions and limits
Least-privilege credentials, read tools kept separate from write tools, and rate and spending limits on every run.
Human approval for risky actions
Anything that sends, pays, deletes or changes customer data can require a person to approve it first.
Evaluations before launch
A test set built from your real cases, scored on every prompt or model change, so quality is measured instead of guessed.
Failure handling
Timeouts, retries with backoff, fallback models and a clean hand-off to a person when the agent is unsure.
Logging and monitoring
Every run is traced: inputs, tool calls, outputs, cost and latency. Alerts fire when error rates or costs rise.
Data security
Business API tiers that don't train on your data by default, PII redaction where needed, and deployment in your cloud account if required.
Cost control
The right model for each step, caching and token budgets, with a monthly report of what each workflow costs to run.
Selected AI Work
Client names are withheld under NDA. On a call we can walk you through the architecture and, where the client agrees, introduce you to them.
Multi-agent RFP analysis and proposal drafting
Built for: A government-contracting software platform
Bid teams spend days reading solicitation packages. We built a LangGraph pipeline in which specialised agents download the documents (PDF, Word and scanned files via OCR), extract every requirement chunk by chunk, merge and de-duplicate them, compare them with the bidder's company profile to turn gaps into clarifying questions, and draft proposal sections.
Python
FastAPI
LangGraph
OpenAI Agents SDK
OpenAI
Tesseract OCR
MongoDB
AWS S3
LLM code-intelligence platform
Built for: A developer-tools product
Connects to GitHub repositories and keeps an up-to-date explanation of the codebase: file and function summaries tied to each commit, plus data-structure and algorithm analysis with complexity notes. Results stream to the dashboard in real time, with rate limiting on model calls.
Node.js
TypeScript
OpenAI
MongoDB
WebSockets
RAG proposal and catalog generator
Built for: A B2B services business
A conversational assistant that gathers project details through chat and forms, retrieves relevant past work from a vector index, and generates tailored proposals or product catalogs.
Node.js
LangChain
Pinecone
OpenAI
Google Gemini
MongoDB
How an AI Agent Project Runs
01
Scoping call
We pick one workflow with a clear owner, a measurable outcome and data we can access. If an agent is the wrong tool, we say so.
02
Pilot on your data
Usually 2–4 weeks. A working agent on real examples, read-only where possible, plus an evaluation set that defines what good looks like.
03
Hardening
Integrations, permissions, approval steps, failure handling and monitoring, tested against the evaluation set.
04
Production launch
A staged rollout: shadow mode first, then a share of real traffic, then full volume once the numbers hold.
05
Run and improve
Ongoing monitoring, re-evaluation when models change, and new tools or workflows as the agent earns trust.
Have a Workflow an Agent Should Run?
Our AI Technology Stack
We design for model flexibility, so you can change providers without rewriting your system.
Models
OpenAI GPT models
Google Gemini
Agent frameworks
LangGraph
LangChain
OpenAI Agents SDK
Retrieval and data
Pinecone
MongoDB
Document AI
Tesseract OCR
PyMuPDF
Google Cloud Vision
Backend and cloud
Python
FastAPI
Node.js
TypeScript
AWS
Engagement Models and Pricing
Every engagement starts with a free scoping call. Prices are in US dollars.
AI pilot
From $5,000
One workflow, built on your data in about 2–4 weeks, with an evaluation set and a go/no-go report.
Production build
Quoted after the pilot
Fixed price or time and materials. Integrations, approval steps, monitoring and rollout, scoped from what the pilot proves.
Dedicated AI team
$25–49 per hour
AI engineers who work as part of your team, billed monthly, for ongoing agent and product work.
Agentic AI Development: Frequently Asked Questions
What is agentic AI development?
Agentic AI development is building software in which a large language model plans steps, calls tools such as APIs, databases and documents, and completes tasks with limited supervision. Expedey builds these agents for production use, with evaluations, monitoring, permissions and human approval steps around the model.
What is harness engineering?
Harness engineering is building the system around an AI model that turns it into a dependable agent: the agent loop, tool definitions and permissions, context and memory management, guardrails and human approval, evaluation sets, and logging and monitoring. The model provides the reasoning; the harness decides what it can see, what it can do and how its work is checked. Expedey engineers harnesses for every agent it ships.
How is an AI agent different from a chatbot?
A chatbot answers questions. An AI agent also acts: it can look up records, update systems, draft documents and run multi-step workflows. That is why agents need tool permissions, approval steps and monitoring that a chatbot does not.
How much does it cost to build an AI agent with Expedey?
A focused pilot starts at $5,000. Production agents with several integrations cost more, depending on the number of systems, the data and the accuracy required. Our rates are $25–49 per hour. See our AI agent cost guide for worked examples.
How long does it take to build an AI agent?
A pilot on your own data usually takes 2–4 weeks. Taking an agent to production with integrations, evaluations and monitoring usually takes a further 4–10 weeks, depending on scope.
Which AI models and frameworks does Expedey use?
We have shipped agents with OpenAI GPT models and Google Gemini, orchestrated with LangGraph, LangChain and the OpenAI Agents SDK, using Pinecone and MongoDB for retrieval and Python (FastAPI) or Node.js backends. We choose the model per task and keep systems provider-flexible.
How do you keep AI agents safe and reliable in production?
Each agent gets least-privilege permissions, human approval for risky actions, an evaluation set scored on every change, failure handling with retries and fallbacks, and full logging of inputs, tool calls, outputs and cost.
Is our data secure, and will it be used to train AI models?
We use model providers' business API tiers, which do not train on your data by default. We can redact personal data before it reaches a model and deploy the system in your own cloud account.
Can you add AI agents to our existing software, CRM or ERP?
Yes. We connect agents to the systems you already use through their APIs or databases, starting read-only and adding write actions behind approval steps.
Where is Expedey located?
Expedey has offices in Houston, Texas (USA) and Karachi, Pakistan, and works with clients remotely. We agree on overlapping working hours with each client at the start of a project.
Do you support AI agents after launch?
Yes. We offer monthly support that covers monitoring, re-running evaluations when providers release new models, prompt and tool updates, and new workflows.
