Questions executives actually ask.

Olyra is an AI strategy, operations, and implementation partner. We help organizations decide where AI creates real value, set the governance to use it responsibly, and then implement it — AI agents, operational intelligence systems, and workflow automation — integrated with the systems teams already use. Engagements typically start with a fixed-scope AI Operations Audit and follow the Olyra 120, a four-phase method designed to produce first measurable wins within 30 days and working, measured systems within 120. Offices in Miami and São Paulo; clients across North and South America.

Olyra was founded and is led by Ana Fernandes, whose background spans project management, software delivery, and IT leadership in healthcare and enterprise environments, with ITIL and Scrum certifications and working fluency in English, Portuguese, Spanish, and French. Engagements are senior-led: the people who scope your work are the people who deliver it, supported by a growing team across our Miami and São Paulo offices. You will never be handed off to a bench of juniors after the sales call.

Three ways. First, we implement: engagements are scoped to end with working systems in production, not a recommendations deck. Second, we're operationally grounded: work starts from your workflows and systems, stays vendor-neutral, and uses the smallest technology that solves the problem — sometimes that isn't AI at all. Third, speed and accountability: the Olyra 120 method targets first measurable wins within 30 days, every workflow gets a baseline before we touch it, and results are reported against that baseline. You get big-firm discipline without the big-firm timeline or invoice.

We focus on operations-heavy sectors: manufacturing, logistics and distribution, healthcare, professional services (legal, accounting, consulting), financial services and insurance, and retail — including franchise and multi-location organizations. The common profile matters more than the label: meaningful document volume, repetitive administrative work, multiple systems that don't talk to each other, and leadership accountable for operational performance. If that describes your organization, the method applies even if your industry isn't listed.

Primarily mid-market organizations — roughly 50 to 1,000 employees, or smaller firms with disproportionate operational complexity (multi-entity, multi-location, document-heavy). Below that range, off-the-shelf tools usually suffice and we'll say so. Above it, we work with divisions and business units that want implementation speed their enterprise programs can't deliver. The honest qualifier is operational scale: enough volume that a workflow improvement pays for itself.

A fixed-scope, two-week diagnostic of how work flows through your organization. We interview the people doing the work, map workflows and systems, review data readiness and governance gaps, and deliver an AI Opportunity Map — every significant opportunity scored by impact, effort, and risk — plus a 120-day implementation roadmap with quick wins identified. It's designed to give executives a factual basis for AI decisions before any spend on tools or vendors.

Typically 6–10 total staff hours across two weeks: a leadership kickoff, short structured interviews with workflow owners and the people doing the work, and a guided systems walkthrough with whoever knows your data. We schedule around your operation and consolidate sessions where possible. The deliberate design goal is maximum insight per hour of your team's time — disruption during discovery would contaminate the very baselines we're measuring.

It's a fixed fee, agreed on the scoping call before any work begins — the price depends on organization size and how many departments and workflows are in scope. There are no change orders and no obligation to continue afterward. Most clients treat it as a self-contained purchase: the roadmap is written to be executable by us, by your internal team, or by another partner.

Then that's what the report says. Part of the audit's value is the ranked "not now" list: opportunities that look attractive but fail on data readiness, risk, or economics. If nothing clears the bar, you'll know why, what would have to change, and what non-AI improvements (process fixes, integrations) would pay off instead. An honest no protects you from the expensive maybe — and it's why the audit is priced as a product, not a sales call.

No. The roadmap is deliberately written so any competent team can execute it — with named workflows, systems, sequence, and success measures. Some clients implement internally; some engage us for the first Olyra 120 cycle; some do a mix, keeping us on the complex integrations. Phase boundaries in the method are clean handoff points, so you can change approach between phases without losing work.

A free consultation is a sales conversation; the audit is diagnostic work with deliverables. We put real hours into interviews, workflow mapping, systems review, and value modeling — and you receive artifacts (Opportunity Map, readiness review, roadmap) that survive contact with your CFO. Free advice tends to recommend whatever the advisor sells. A fixed-fee diagnostic with no implementation obligation is structurally built to tell you the truth.

The Olyra 120 targets working, measured systems within 120 days: assessment and quick wins in the first 30, a proven pilot by day 60, production build by day 90, deployment and measurement by day 120. Simple workflows finish faster; complex integrations or regulated review steps can extend specific phases — and we flag that during design, not after. The structural guarantee is checkpoints: you're never months in without something working.

Our engagements are designed to remove work people shouldn't be doing — re-keying, chasing documents, assembling reports — not to remove the people. Practically, clients redeploy recovered hours to backlogs, service levels, and growth they'd been deferring. We build with your team from day one, train them on the new workflows, and design human review into every step where judgment matters, because workforce adoption is what makes the results stick.

The systems mid-market operations actually run: ERPs (from SAP and NetSuite to legacy and niche vertical systems), CRMs, finance and accounting tools, document stores, email platforms, and the spreadsheet layer that connects them today. Integration paths vary — APIs where they exist, exports, database views, or middleware where they don't — and discovery establishes what's safely reachable before we commit to a design. A system replacement is never a hidden prerequisite.

Pilots exist to fail cheaply. Each one has explicit success criteria set in advance — accuracy, time saved, adoption — and runs on real historical data before touching live operations. If a pilot misses, the go/no-go review says so, we diagnose whether the fix is data, design, or feasibility, and either iterate once with a specific hypothesis or move to the next-ranked opportunity. What you don't get is a zombie pilot consuming budget because nobody wants to admit it missed.

It can be — if boundaries are engineered rather than assumed. In every engagement we define, with your IT and legal: which data each tool may access, where output may go, what is logged, and what may never leave your environment. We favor configurations where your data is not used to train third-party models, document the full data path for review, and implement audit logging on AI interactions. Security posture is part of phase-one governance, so nothing reaches production that your team hasn't approved.

No. We don't build products on client data, and we configure third-party tools so your data is excluded from model training wherever the provider supports it — which is a selection criterion when we recommend tools. Data handling terms are documented per tool in the governance pack, in plain language, so your counsel can verify rather than trust.

You do. Workflows, integrations, dashboards, prompts, documentation, and configurations built in an engagement belong to your organization, and handover with runbooks and training is a standard phase-four deliverable. Where third-party platforms are involved, licenses sit in your name. The test we design to: if Olyra disappeared tomorrow, your team could run, maintain, and extend everything we built.

Fixed-fee for defined-scope work: the AI Operations Audit and each Olyra 120 phase are priced before they begin, based on scope — organization size, workflows, systems involved. Ongoing advisory (governance reviews, optimization) is a flat monthly retainer. We don't bill open-ended hourly engagements, because they reward slowness. Every proposal shows what's included, what's excluded, and what would trigger a re-scope, so the number your CFO approves is the number on the invoice.

Two doors. If you want a factual basis first: request the AI Operations Audit — two weeks, fixed fee, and you'll know exactly where AI pays off in your operation. If you want a conversation first: book a 30-minute intro call — no pitch deck, bring your hardest questions. Either way you'll leave with something useful, and nobody will chase you with a sequence of follow-up emails.