AI integrated into your product: LLM features in production, not PoCs
We embed AI capabilities inside your existing app or SaaS via API — generation, summaries, classification, semantic search and in-app assistants — with evals, guardrails and token costs under control. As a software factory, we ship to production, not to a lab.
Integrating AI into your product means adding features powered by large language models (LLMs) inside the app or SaaS you already have, consuming APIs like OpenAI or Anthropic: text generation and rewriting, automatic summaries, classification and tagging, semantic search with embeddings, and in-app conversational assistants with function calling.
We do not replace your product or sell you a demo: we add AI capabilities to your code with the engineering quality production demands — systematic evaluation (evals), guardrails against hallucinations and sensitive data, token cost control and observability. We are a software factory founded in 2018 (Monterrey + Texas, CMMI Level 2, 5.0★ on Clutch, 200+ projects). We work with a fixed budget and deliverables every 2 weeks, and the code is 100% yours from the first commit.
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 it includes
AI feature design
We define what the LLM solves inside your product (generation, summarization, classification, semantic search or assistant), the user flow and the prompts, before touching production code.
API integration
We connect your app to OpenAI or Anthropic: streaming calls, function calling for real actions, embeddings for semantic search, and robust handling of errors, timeouts and retries.
Evals & quality
We build an evaluation set with real cases to measure response quality objectively, catch regressions when prompts or models change, and decide with data rather than gut feel.
Guardrails & security
Input and output validation, hallucination mitigation, filtering of sensitive content and data, and limits so the model stays within your product’s scope and tone.
Cost control & caching
Token consumption estimation and monitoring, picking the right model per task (not the most expensive by default), response caching and usage limits so AI cost stays predictable.
Observability
Traces for every model call, latency/cost/quality metrics, and logs to debug in production — backed by our internal ARIA platform within the QA cycle.
How we work
Use-case discovery
We identify which AI feature delivers real value inside your product and define success metrics. Output: scope, fixed budget and timeline before coding.
Prototype with evals
We build a prototype of the feature with initial prompts and, in parallel, the eval set that measures its quality — to validate with data before investing in production.
Production integration
2-week sprints: we integrate AI into your code with streaming, function calling and embeddings, mandatory code review, CI/CD and a functional demo each cycle.
Guardrails & cost control
We add input/output validation, hallucination mitigation, caching and token limits; we measure real cost and latency and tune model or prompts.
Launch & observability
Controlled deployment with traces, cost/quality metrics and active monitoring, plus handoff of the full repository — 100% yours from the first commit.
Tech stack
The tools and platforms we build it with — chosen for your problem, not for hype.
Frequently asked questions
How much does it cost in tokens, and how do you control spend and latency?
AI cost depends on the model, prompt size and usage volume. We make it predictable: we estimate consumption before launch, pick the right model per task (not the most expensive by default), use caching and usage limits, and monitor cost and latency in production. For latency we use streaming, so the user starts seeing the response immediately instead of waiting for the model to finish.
What happens to my users’ data privacy?
Your users’ data is not used to train third-party models: we configure it with the enterprise APIs from OpenAI or Anthropic, which offer zero or controlled retention. We apply data minimization (only sending what the model needs), filter sensitive information before the call, and keep traceability. If your case requires it, we evaluate deployment options with stronger residency and isolation guarantees.
Which AI model is right for my case?
There is no single model: it depends on the task, required quality, latency and cost. A classification or short summary is usually solved with a smaller, cheaper model; an assistant with reasoning or complex function calling may justify a more capable one. We work with OpenAI and Anthropic and choose based on eval data — not hype. We leave the integration ready to switch models without rewriting your product.
How do you avoid hallucinations and off-topic answers?
With guardrails and evals, not wishful thinking. We constrain the model with instructions and context retrieved from your own data (instead of asking it to "make things up"), validate outputs, filter out-of-scope content, and measure quality with an evaluation set of real cases. That lets us catch regressions when we change a prompt or a model, rather than discovering errors in production.
Why a software factory and not an AI consultancy?
Because integrating AI into a real product is, above all, software engineering: APIs, error handling, testing, security, cost and maintenance. We are a CMMI Level 2 factory that ships features to production with a fixed budget, deliverables every 2 weeks, and QA on our internal ARIA platform. We do not hand you a PoC that dies in the lab: we leave AI working inside your product, with code 100% yours.
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