PROBLEM
Five real pains
of teams already running AI.
This isn't the abstract worry of a team thinking about starting. It's what shows up in month two, three, or six of AI in production, and what a typical in-house team can't handle on its own.
Silent model drift
Performance drops with no alert. By the time someone notices, three retraining cycles have gone by that nobody ran.
Inference cost out of control
Tokens, GPU, API calls: pennies add up to a monthly bill nobody budgeted for. There's no AI FinOps.
Scarce in-house AI team
Hiring a senior ML engineer takes months; keeping one takes longer. And they're too expensive to run operations; they want to build.
Constant regulatory pressure
BACEN, ANS, LGPD, sector frameworks. An audit isn't a one-time event; it asks for logs, evidence, and process every quarter.
Integrations that break on upstream changes
A source system changes its contract, the model stops receiving data, and decisions stop flowing. Whoever runs it is left firefighting.
SOLUTION
Five operational deliveries
paired to the five pains.
Structured operations, not firefighting. Every real pain maps to a recurring squad process, with a set cadence, format, and owner.
Continuous monitoring + scheduled retraining
Drift, quality, and performance under permanent tracking. Retraining runs on a rule (a trigger or the calendar), never on a hunch caught too late.
Inference FinOps
Caching, the right model for each case, batching, and the call between Bedrock and self-hosted. Cost per decision tracked each month, with a forecast for the next quarter.
A senior squad as an extension of your team
Data engineering, ML, generative AI, and MLOps in a shared squad, with one anchor person dedicated to your account. Better cost than an equivalent in-house team, with production depth a small team rarely matches.
Continuous compliance
Auditable logs, White Box AI, and governance evidence ready for a sector audit. LGPD, BACEN, ANS, where they apply, with the sector framework behind them.
Living integration maintenance
Upstream changed? The squad adjusts the contract, validates end to end, and ships with a rollback ready. No break in the decision flow.
USE CASES
Three contexts where
AaaS makes the difference.
AaaS isn't generic. It covers specific situations where continuous operations are the pain, not the opportunity.
A Solution Pack already in production
A client who deployed a BlueMetrics Solution Pack and wants to keep it running without building an in-house team. AaaS operates it, evolves it, and responds as the business changes.
A Custom Project after go-live
A tailor-made solution that reached production. The client chooses: run it with an in-house team (with a knowledge transfer) or contract AaaS for continuous operations. Both are valid paths.
A legacy system from another vendor
AI built by another vendor that landed in production without proper operations. The classic "delivered, but nobody runs it" case. AaaS stabilizes it, audits it, and takes over without a rebuild.
WHAT YOU GET EVERY MONTH
Five recurring deliverables.
Fixed format, fixed cadence.
Operations you can predict, both down into the team and up to the board and the auditor.
OPERATION CYCLE
Not a timeline.
A loop.
Continuous operations has no single "go-live". It runs in short cycles of monitor → detect → act → report, and the next cycle starts before the last one closes.
Monitor
Permanent tracking: drift, performance, cost, integrations, compliance.
Detect
Drift, an opportunity, or a risk, caught by automation or in the weekly squad review.
Act
Retraining, a prompt change, a model swap, an integration fix, with a rollback ready.
Report
Evidence logged, the decision documented, the metric updated. The input to the next cycle.
WHY OUTSOURCE
Four reasons not to
build an in-house team.
AaaS isn't "instead of an in-house team". It's "instead of trying to build one and finding out you can't". We're honest about that.
Senior shared squad
Better cost than an in-house ML engineer, MLOps, and data engineer combined. Production depth a small team can't reach on its own.
Mature MLOps
The pipeline is already up: observability, retraining, quality gates, drift, FinOps. You don't build it; you inherit it working.
AWS production knowledge
Years running AI in production inside AWS accounts. Advanced Partner, with expertise in SageMaker, Bedrock, Textract, cost, and governance.
Works with others' legacy
The AI doesn't have to be ours. We operate third-party systems already in production: we audit them, stabilize them, and take over.
STACK & GOVERNANCE
AWS-first.
Isolation in the client's account.
Operations run inside the client's AWS account, with environment isolation, auditable logs, and White Box AI from the day we take over. The operations stack includes MLflow, SageMaker, Bedrock, and our own FinOps instrumentation. Where it applies, we follow the sector's regulatory frameworks (LGPD, BACEN, ANS, ANEEL). When a client already has a consolidated stack on another cloud, we operate from there. We don't treat AWS as universal; we treat it as a well-grounded technical default.
QUESTIONS FROM OPERATORS
FAQ
No. We also operate legacy AI systems from other vendors that reached production and need continuous operations. The starting point is an operations diagnostic: we assess the current state of your AI in production, its technical debt, and its risks before we take over. A common case is a solution from another vendor left "delivered but not operated"; we audit it, stabilize it, and take over.
An annual contract with scalable monthly packages and a contracted SLA. No "consultant hours": the client buys operating capacity (models operated, retraining cycles, response SLA), not someone's time. Packages are sized by the initial diagnostic and reviewed each quarter as the roadmap evolves.
A squad shared across clients, with an anchor person dedicated to your account, a senior ML engineer or an architect depending on the contract. The total cost beats building an equivalent in-house team (data engineer, ML engineer, MLOps, governance), with production depth a small team rarely reaches on its own.
Monthly: a performance report (models, cost, incidents, governance) and a one-hour technical alignment meeting with the anchor person. Quarterly: a cost forecast for the coming cycles with explicit assumptions, and a technical roadmap aligned to the business roadmap. Under SLA: squad access for incidents, adjustments, and technical questions.
Operations account for the applicable regulatory framework (LGPD, BACEN, ANS, ANEEL, sector frameworks). Auditable logs, environment isolation, and White Box AI give you the traceability an audit requires. Evidence is generated continuously, not "assembled right before the audit"; when the auditor arrives, it's ready.
The client does. AaaS operates inside the client's AWS account, on code and models the client owns. The operation is ours; ownership of what's in production is theirs from day one, even when we're operating AI built by another vendor. We don't require an ownership transfer to take over.
SIDE EXIT
Don't have AI in production yet?
AaaS operates what's already running. If your case is "we want to start with AI", the entry point is a Solution Pack (for a recurring case) or a Custom Project (for a one-off).