AI implementation for your business
ozeida implements AI for businesses: we connect large language models (LLMs) to the systems and data you already have so they can answer, classify, draft or execute steps of a process, with per-user permissions, an audit trail and a capped cost. Every project starts with an agreed metric and a pilot with an end date. We are a nearshore team in Saltillo, Mexico, working in US Central time.
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What we solve
Assistants grounded in your data
An assistant that answers your team or your customers from your documents, catalogs, policies and databases, citing the source of every answer. For internal support, customer service, sales and HR.
Agents that execute steps
An agent (a program that uses an LLM to decide and execute steps) that builds a quote, creates an order, reconciles invoices or prepares a report by querying your systems through APIs. Actions with consequences (send, charge, delete) are confirmed by a person or a fixed rule.
Process automation
Triage emails and tickets, extract data from invoices, contracts and purchase orders, draft replies and summaries, route requests.
AI over company data
Natural-language questions about sales, inventory or operations, with answers computed from your data rather than guessed.
Integrations
ERP, CRM, email, WhatsApp Business, SharePoint/Drive, SQL databases, spreadsheets. If it has an API or a database, we connect it.
Before you start
Before implementing AI in a business you need three things: a clear use case (one specific process with an owner), usable data (you know where it lives, who owns it and what shape it's in) and an impact metric with a baseline (how long, how much or how often that process costs you today). If one is missing, the first phase of the project is getting it. Nothing gets built on top of what doesn't exist.
When does it make sense to implement AI in a business?
When all three conditions hold at once:
- A clear use case. A process someone does by hand today, with a start and an end. "Answer quote requests that arrive by email" is a use case; "use AI in sales" is not.
- Usable data. The documents, records or conversations that process depends on exist, can be accessed and are good enough. An ERP with orders entered correctly works; a folder of hand-scanned PDFs sometimes doesn't.
- An impact metric. A number you can measure today and that the project has to move: response time, hours of data entry, share of requests resolved without a person, cost per query.
If one is missing, we say so on the first call. Getting the data in order first is cheaper than maintaining an assistant that answers badly.
What counts as "usable data"?
Three minimum conditions, which we call the data baseline:
- A reliable source. You know which system each piece of data comes from and who maintains it (the ERP, the CRM, a SQL database, a mailbox, SharePoint).
- Verifiable quality. You can check, on a real sample, that the data is complete and current for the chosen process.
- Traceability. Every answer the system gives can point to the document or record it came from. Without that, nobody can review whether the AI got it right.
What a well-framed case looks like:
| Process | Use case | Data it needs | Metric and baseline |
|---|---|---|---|
| Quotes by email | Draft a quote from the customer's email | Current price list, quote history, discount rules | Time from email received to quote sent |
| Internal support | Answer policy and procedure questions from staff | Manuals and policies in a versioned document repository | Questions resolved without a person |
| Accounts payable | Extract vendor invoice data and propose the entry | PDF or XML invoices, vendor master, purchase orders | Data-entry hours per week and errors caught in review |
These are examples of framing, not published cases.
What if I don't have all three?
It becomes the first phase of the project, with its own scope and cutoff date:
- No clear use case: a diagnostic session to pick one process and write down what's in and what's out.
- No usable data: a short phase to connect or clean the source (export from the ERP, structure documents, decide who maintains what).
- No metric: measure the current state for an agreed period before building. If it can't be measured, it isn't promised.
How is the result of an AI project measured?
The metric is defined in the diagnostic (time, cost or accuracy) and the baseline is measured before any code is written. The pilot is compared against that baseline on its cutoff date, and in production the same metric keeps being tracked along with cost per query and spend against the monthly cap. If the pilot doesn't move the metric, we say so and stop.
How we work
Discovery (one call + one working session)
Pick one process, define the metric (response time, hours freed, resolution rate without human intervention, cost per request) and measure the current baseline.
Pilot (weeks, fixed price)
Build the minimum version connected to real data, with an evaluation set and real users. Fixed end date.
Decision with data
Compare against the metric. If it meets it, it goes to production. If not, we say so, hand over what we learned, and stop.
Production
Per-user permissions, audit log, monthly spending cap with automatic cutoff, monitoring and alarms. Deployed in your AWS account if you prefer.
Operations and improvement
Ongoing evaluation against the test set, answer review, cost tuning. Optional, not mandatory.
What you get
- A production application with the code in your repository.
- An evaluation set for answer quality that runs before every change.
- A usage and cost panel: requests, cost per request, spend against the cap.
- Documentation of what it does, what it doesn’t, and how to operate it.
- Monitoring with ozeida Quality Control when the project needs it.
Security and data
Data minimization
Models receive only what’s needed to answer; personal data is anonymized where possible.
Providers under enterprise terms
Models through Amazon Bedrock or other providers that, per their public documentation, do not train on customer data.
Your account, your data
Deployment into your own AWS account when required, in the AWS region your policy demands.
Per-user permissions
The assistant sees what the user is allowed to see, nothing more.
Audit trail
Every request and every action is logged.
A cap, not a surprise
The monthly inference budget (what it costs to use the model) is an application setting: it warns at 50% and 80% and cuts off at 100%. Cost per request is reported from the pilot onward, so the production decision is made with numbers.
AI implementation FAQ
Does the AI make things up?
A model without context does. That’s why every answer is grounded in your documents and data (retrieval with citations), an evaluation set runs before every change, and actions with consequences go through a person or a fixed rule, not the model.
Is my data used to train models?
No. We use models through Amazon Bedrock or other providers under enterprise terms that do not train on customer data, per their public documentation. Models receive only what’s needed, and the application can run in your own AWS account.
How long does a pilot take?
Weeks, not months: scope is closed during discovery and the pilot has a fixed end date. The exact timeline depends on the process and the integrations.
What if the pilot doesn’t meet the metric?
We say so, hand over what was learned (data, tests, what did work) and stop. Nothing goes to production without being measured.
Why a nearshore team in Mexico for AI work?
Business hours that overlap with US Central and Eastern time (Saltillo is UTC−6 year-round), English as the working language, and a short flight from Texas. You talk directly to the engineers who design and write the code.
Why not just use ChatGPT?
A generic chat doesn’t know your orders, inventory or customers, doesn’t respect per-user permissions and leaves no audit trail. AI implementation is precisely connecting the model to your data and systems with those rules.
How do you control cost?
The monthly inference budget is an application setting with warnings and automatic cutoff. Cost per request is reported from the pilot onward.
Which models do you use?
Whichever meets the metric at the lowest cost for your case; usually models available on Amazon Bedrock, and other providers when it makes sense. They are compared during the pilot on your data, not picked from a catalog.
When does it make sense to implement AI in my business?
When there is a clear use case, usable data and an impact metric with a baseline. If any of the three is missing, that part gets solved first, in a short phase with its own scope, before building the assistant or agent.
What do I need in place before starting an AI project?
A minimum data baseline: a reliable source, quality you can verify on a real sample, and traceability back to the original document or record. If it doesn't exist, we include it as the first phase of the project, with a cutoff date, instead of building on top.
How do you measure whether the AI project worked?
With the metric agreed in the diagnostic and its baseline, compared at the pilot's cutoff date and then in production. We don't publish improvement percentages that don't come from a real measurement.
How much does an AI project cost?
It depends on three things: data availability and quality, the complexity of the use case, and how much integration with your systems it needs. The pilot has a fixed price set in the diagnostic together with the metric; model usage in production runs under a monthly cap with an automatic cutoff.
You may also need
Custom software
Web applications, customer and vendor portals, internal tools, APIs and integrations.
See custom softwareAWS cloud & DevOps
Serverless architecture, migration, infrastructure as code, CI/CD with real gates, cost control, security baseline.
See AWS cloud & DevOpsSRE & observability
Monitoring, complete alarm views, service-level objectives, incident response, postmortems.
See SRE & observability
Which process should the AI handle?
A 30-minute call to pick the process, define the metric and tell you whether a pilot makes sense.
Tell us about your project