01 · Agents
Internal agents and copilots
They run real workflows against your systems — role-based permissions, defined limits, and an audit trail for every action — instead of answering one-off questions.
Embedded AI — senior engineers inside your operation
Turn AI into operational results, not another pilot.
We train your teams, find the right use cases, and build production-ready AI systems inside your real workflows.
Fixed scope · Fixed pricing by scope · Bilingual delivery · Controls & compliance from day one
Inputs
- Documents
- ERP · CRM
- Tickets
- Internal data
- 01Scan
- 02Scope
- 03Build
- 04Embed
Production
- Internal copilot
- Operations agent
- Approved automation
- ROI dashboard
GovernanceHuman reviewEval framework
Built on your stack and your data
Agnostic by design: we never lock you into a single AI vendor. We choose model and cloud based on your case, infrastructure, and regulation — not our partnerships.
LFPDPPP complianceMexico-resident delivery · ES + EN
All major model providers:Claude (Anthropic)GPT (OpenAI)Gemini (Google)Llama (Meta)MistralDeepSeek
All major clouds:AWS (Bedrock)Microsoft Azure (Azure OpenAI / Foundry)Google Cloud (Vertex AI)On-premHybrid
The current state
The model is rarely the problem. Integration almost always is.
95%
of AI pilots produce no measurable P&L impact.
Source: MIT, “The GenAI Divide: State of AI in Business 2025” (Project NANDA).
90days
to validate, build, and measure a first system in production.
Source: the Fragua methodology’s delivery commitment.
ChatGPT, Copilot, and Claude are powerful tools, but on their own they don't change a collections workflow, a support operation, a supply chain, or a legal process. Value appears when AI connects to real data, permissions, systems, people, and metrics.
Almost no pilot dies from the technology; it dies because it was never designed to operate. A generic copilot doesn't solve this on its own either: it still has to be wired into your processes, data, permissions, and metrics. We design for production from day one.
Why now
Mexico already has more than enough AI demos. What's missing is who takes them to production.
The market already understands that AI matters. The question now is where to start, which use cases to prioritize, how to train people, how to integrate systems, and who's accountable for getting it to production.
01
Executive pressure
Boards are already asking about AI. Teams need to show real progress in production.
02
Talent gap
Companies can't hire enough AI talent, and senior profiles are scarce and expensive.
03
Nearshoring + infrastructure
Mexico is attracting cloud investment, nearshore operations, and rising demand for local automation.
04
Governance & compliance
Enterprise AI requires privacy, traceability, human oversight, and regulatory readiness.
Measurement, not faith
We don’t ship “it seems to work.” We ship a scorecard.
Before we build, we measure the baseline: how long the process takes today, what it costs, where it fails. During the build, we evaluate every version against real examples from your operation — accuracy, retrieval quality, hallucinations, latency, and cost. In production, a live ROI dashboard shows impact against that baseline.
If it can’t be measured, we don’t sell it.
Illustrative example
| Metric | Baseline | v04 |
|---|---|---|
| Accuracy | 64% | 87% |
| Retrieval quality | 51% | 82% |
| Invented answers | 18% | 4% |
| Response time | 14 s | 2.1 s |
| Cost per operation | $3.80 | $0.90 |
01
Baseline measured before writing a single line of code
02
Evaluation suite built from real cases in your operation
03
Per-version scorecard: accuracy, hallucination, latency, cost
04
Production ROI dashboard, visible to your leadership
Request a diagnostic →30-minute call · we reply within one business day
What we do
AI systems that reach production, not pilots that stall at the demo.
Train, validate, build, and embed — one complete system that closes the gap between AI ambition and operational reality.
Upskill
We upskill teams by role
Discover
We prioritize the use cases worth doing
Build
We build tools ready to operate
Embed
We embed AI engineers in your organization
02 · Evaluation
Evaluation suite
Your real data, a measured baseline, and tests that run on every change. If something degrades, it does not ship.
03 · Retrieval
Search over your documents (RAG)
Answers grounded in your documents, with the source cited and the permissions of whoever is asking.
04 · Integration
ERP, CRM and document integrations
We connect AI to the systems your operation already runs on, in both directions: reading and writing.
05 · Documents
Document automation
Extraction and classification for invoices, contracts and case files, with human review wherever the risk demands it.
We also build the things that do not fit on five cards. Tell us your case
Productized services
Scope defined before we start. Pricing, in a 30-minute call.
Duration1–2 days
From zero to a working workflow on your data, in two days.
- Intensive workshop with your team and your real data
- A working AI workflow by the end of day 2
- Prioritized use-case matrix (impact / effort / risk)
- Next-step recommendation with scope and expected ROI
Duration4–6 weeks
For leadership and functional teams ready to use AI safely and productively.
- 4 live workshops
- Role-based prompt libraries
- Internal AI playbook
- Governance guidelines
Best starting pointOpportunity Scan + Prototype
Duration6–8 weeks
For companies that need clarity on where AI can create measurable value.
- Interviews across 8–15 stakeholders
- Workflow & systems map
- Top 5 prioritized use cases
- ROI hypotheses
Duration8–12 weeks
For teams ready to ship one production-grade AI workflow.
- Production workflow or platform
- Integration with your stack
- Governance & eval framework
- Testing & adoption support
Duration3-month min.
For ongoing delivery, optimization, and capability transfer.
- 1–2 embedded AI engineers
- Continuous development
- Internal team mentoring
- Continuous updates for new models (prompts, evaluation suites, model selection)
Every scope is defined before we start, with a fixed price by range based on scope — the Bootcamp is the lowest-investment entry. No open-ended hourly billing. No consulting surprises.
Decorative animation: a character field goes from frozen to lit and advances generation by generation.
A living system
Models improve every few months. Your systems should improve with them.
The system stays live after launch. We continuously tune prompts, evaluation suites, and model selection — before rollout and throughout operations — so every new model generation makes your systems better, not obsolete.
The Forward-Deployed model
Senior engineering inside your operation, until the system runs.
A Forward-Deployed AI Engineer is a senior engineer who works inside the client's real context: processes, systems, data, permissions, constraints, users, and internal politics.
Unlike traditional consulting, they don't separate strategy from execution. They discover, design, build, test, deploy, and transfer knowledge alongside your team.
Traditional consulting
Forward-Deployed AI
Long diagnostics
Build-oriented diagnostics
Slides before software
Real prototypes & systems
Leveraged junior teams
Senior practitioners hands-on
External delivery
Work inside your context
Vendor dependency
Capability transfer
Abstract roadmaps
Metrics, adoption & production
Use cases by function
Practical relevance in every part of the business.
Teams lose hours searching for procedures and scattered operational knowledge.
Lower resolution time and better shift-to-shift transfer.
Analysts spend hours on manual reconciliation and finding policy answers.
Faster monthly close and fewer reconciliation errors.
Agents are slow to find answers and summarize long cases.
Lower handle time and higher first-contact resolution.
Slow manual search of clauses and regulatory answers.
Faster legal responses with verifiable sources.
Employee support is repetitive and training doesn't scale.
Higher adoption and lower first-line support load.
Reps spend time on research and writing proposals from scratch.
More selling time and better proposal quality.
See all 30 use cases in detail →
Request a diagnostic →30-minute call · we reply within one business day
Impact patterns
The kind of outcomes we build.
Three workflow patterns we know how to build: the scope of each, and what changes in the operation once it reaches production.
Our first-hand experience: we build and operate our own AI products in production, including legal tools used by Mexican companies.
Manufacturing · multi-plant
Maintenance copilot
Workflow: maintenance & quality knowledge
Fewer escalations
- Faster access to procedures
- Fewer repeated escalations
- Better shift-to-shift transfer
- Clear usage & quality metrics
Financial services · regional
Compliance assistant
Workflow: policy & legal compliance
Faster answers
- Faster legal responses
- Source-grounded answers
- Less manual document search
- Controlled access to sensitive content
Shared services · nearshore
Finance automation
Workflow: finance operations
Swifter closes
- Less manual reconciliation
- Better exception routing
- Faster monthly close
- Internal team enablement
Request a diagnostic →30-minute call · we reply within one business day
What comes next
The first workflow proves the value. The next ones multiply it.
The first system in production isn’t the end of the road — it’s the foundation. Integrations, permissions, evaluations, and governance built for the first workflow are reused on the second. Each new case costs less and ships faster than the last.
01
Smaller teams, more output.
The same teams deliver more work without hiring.
02
More capacity without more hires.
Grow delivery capacity without growing the payroll.
03
Delivery speed.
Shorter close, response, and resolution cycles in every area you add.
04
Better governance.
Every new workflow inherits permissions, traceability, and human review already proven.
05
Data to the surface.
Operational information buried in documents and systems becomes visible and actionable.
Governance, security & compliance
Governance from day one — not a legal appendix.
Process aligned with the LFPDPPP
We protect your customers’ and employees’ personal data (PII): privacy notices, consent, and access control aligned with the LFPDPPP.
Human oversight
We design systems with clear review points, escalation paths, and human accountability.
Model-agnostic architecture
Claude, GPT, Gemini, Llama, Bedrock, Azure OpenAI, on-prem or hybrid as needed.
Secure integration
Connection with least-privilege access, auditability, and documented technical boundaries.
Evaluation framework
Accuracy, retrieval quality, hallucination risk, latency, cost, and feedback measured continuously.
Knowledge transfer
Every engagement includes documentation, training, and internal capability development.
Governance stack
06Business workflow
05Human review
04Evaluation
03Models
02Permissions
01Data
Free resource
Mexico Enterprise AI Implementation Playbook 2026
A practical guide for CIOs, CEOs, and transformation leaders moving from AI experiments to production systems.
- Why AI pilots fail
- How to choose the right first use case
- What to ask a vendor before signing
- How to design governance from day one
- The 90-day path from scan to production
Thank you. We'll email you the playbook at your work address as soon as it's available. If it doesn't arrive, write to hola@fragua.tech.
Frequently asked questions
Objections, head-on.
Next step
Stop evaluating AI in the abstract. Validate a real workflow.
In a 30-minute call we identify where you are, what's blocking adoption, and the three use cases most likely to create value in your organization.
Thanks. We'll reach out within one business day to coordinate your diagnostic.
No commitment · Clear agenda · Practical recommendations · Request the playbook