LLM assistants & agents: that don't just answer — they get things done
We design chatbots, copilots and LLM agents (GPT-4o, Claude) that understand your business and execute real tasks via function calling — open a ticket, query your ERP, book a meeting, update the CRM — integrated into your web, WhatsApp and internal systems, with guardrails so they don't hallucinate or go off-script.
An LLM assistant or agent is a layer of software, built on top of a model like GPT-4o or Claude, that converses in natural language with your customers or your team.
The difference between a bot that merely replies and an agent that actually helps comes down to two things: context and tools. We give it context about your business (policies, catalog, documents) via RAG over a vector database, and we give it tools (function calling) so it can do real things in your systems: look up an order in the ERP, open a ticket, book an appointment, update the CRM. On top we add guardrails and evaluation to bound what it can and cannot do, prevent made-up answers and keep an audit trail of every action. It's not magic and it's not "computer vision": it's serious software engineering around a model, wired into your operation.
Why iTechDev
Fixed budget
Scope and price defined before we start. No hourly billing, no ambiguous scope.
Code 100% yours
All code and configuration are your property from the first commit. No vendor lock-in.
Progress every 2 weeks
Live functional demos each sprint. You see real progress, not a months-long black box.
Engineering with process
CMMI Level 2, 5.0★ on Clutch and 200+ projects. Nearshore team in Monterrey + Texas, in your time zone (CST).
When you need it
What's included
Agent design & model selection
We define the scope, persona and conversation flow, and pick the right model (GPT-4o, Claude or another) based on your case, cost and privacy needs. We don't start from the model: we start from the task it has to solve and where its responsibility ends.
Connection to your tools & APIs (function calling)
We give the agent tools to execute real tasks via function/tool calling: query your ERP/CRM, create or update a ticket, book an appointment, generate a quote, trigger an n8n flow. Each tool is a bounded, audited function — the agent doesn't touch your systems freely.
Memory, context & RAG over your data
We connect your documents, policies and catalog through RAG over a vector database, so the agent answers from your real, cited information rather than what the model "thinks". We add conversation memory so it keeps the thread and the customer's context.
Guardrails & hallucination control
We bound what it can and cannot do or say: input/output validation, restriction to your business domain, escalation to a human when unsure, and rules so it doesn't invent data, prices or promises. The goal is a trustworthy agent in front of customers, not an experiment.
Channels: web, WhatsApp & internal systems
We integrate it where your users already are: a widget on your website, the WhatsApp Business API, or embedded in your internal tools. The same agent logic serves multiple channels, with handoff to a human when needed.
Evaluation, observability & continuous improvement
We set up evals to measure answer and action quality before and after go-live, with logs and full traceability of every conversation and every tool executed. So you know what it answers, what it does, and where to improve — instead of trusting it blindly.
How we work
Use case & data
We start from the business outcome, not the technology: what task the agent will solve, with what data, and which systems it needs to connect to. It's the same approach as our AI assessment — anti-hype, with an honest scope before committing budget.
Prototype on a real case
We build a working agent on your most valuable case, wired to a real tool (not a toy demo), to validate that it understands the context, executes the action and respects the guardrails before we widen the scope.
Integration, guardrails & evaluation
We connect the tools and APIs (function calling), set up RAG over your data, define guardrails and run evals with real and edge cases. We validate quality with our internal ARIA platform and CMMI Level 2-aligned processes.
Launch by channel & handoff
Controlled rollout on the chosen channel (web, WhatsApp or internal), with human escalation, monitoring and a tuning period on real conversations to raise the resolution rate without risk.
Operation & continuous improvement
We leave observability, logs and a process to review conversations, adjust prompts, tools and guardrails, and ship new versions. The code and configuration are 100% yours from the first commit — no vendor lock-in.
Tech stack
The tools and platforms we build it with — chosen for your problem, not for hype.
Frequently asked questions
How do you stop the agent from hallucinating or making up data?
With several layers. First, RAG: the agent answers from your real documents and data, with citations, instead of what the model "remembers". Second, guardrails: we validate inputs and outputs, restrict it to your business domain, and configure it to escalate to a human when unsure rather than invent. Third, evals: we measure answer and action quality with real and edge cases before and after go-live. We don't promise zero errors — no honest vendor does — but we do deliver a bounded, traceable agent that's safe to put in front of customers.
Which model do you use, GPT-4o or Claude? And why one over the other?
It depends on the case, not the trend. We work with GPT-4o, Claude and other models, and choose based on quality for your task, cost per token, latency, context window and privacy requirements. For many cases a smaller, cheaper model well orchestrated beats the biggest model for everything. We settle this in the assessment, with clear criteria and a cost range, rather than assuming the answer up front.
What happens with the privacy of my data and my customers' data?
We design so your data doesn't end up training third-party models: we use enterprise APIs with no-retention/no-training policies, minimize what information leaves toward the model and, when the case demands it, evaluate private deployment or self-hosted model options. RAG keeps your knowledge in your vector database under your control, and we leave logs and traceability of what was queried. We turn this into concrete governance for your case during the assessment.
How much does this cost to run in tokens? Is it expensive?
Token cost is real but is usually a small fraction of the person-hours it frees up. We control it with engineering decisions: picking the right model per task (not the priciest for everything), trimming the context we send, caching frequent answers, and using RAG so we don't stuff whole documents into every call. In the assessment we give you an estimated monthly cost range by volume, so you decide with numbers and no surprises on the bill.
Do you have this actually working, or is it just theory?
We build real agents, not slideware. The most direct proof is our own operation: ARIA, our internal LLM-agent platform, and the AI assessments running on this very site — which converse, evaluate your context and deliver a plan — are systems we designed and operate ourselves. We don't invent client logos: we back it with a CMMI Level 2 certified process, over 200 delivered projects and code that's 100% yours, so you validate the capability with verifiable facts.
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