01
AI quality + observability
AI evaluation and observability
Make quality a release decision, then keep the evidence attached to production.
- Trace instrumentation
- Datasets and evaluators
- Release gates
- Production review cadence
Estma / Production AI systems
Production AI needs quality, capacity and control.
Estma helps startups and growing businesses put AI on a working foundation: evaluation and observability, model inference capacity, and cloud capacity—supported by implementation, security, automation and technical governance.
Three core offers
- 01Evaluation + observabilityQuality evidence and release control
- 02Model inference capacityAnnual API capacity and usage controls
- 03Cloud capacityOperated compute, storage and AI services
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
✳build✳observe✳provision✳secure✳operate
Three core offers
One production system. Three ways to enter the work.
Start with AI quality, model inference, or cloud infrastructure. Estma connects the commercial resource to the architecture, controls and operating work around it.
02
Model APIs + embeddings
Model inference capacity
Commit inference capacity against a workload forecast, then control how the product consumes it.
- Usage forecast
- API and SDK integration
- Quotas and rate limits
- Token-cost controls
03
Compute + storage + AI services
Cloud capacity programmes
Turn cloud capacity into an operated environment with clear ownership, monitoring and cost control.
- Compute and GPU resources
- Storage and managed databases
- Environment controls
- Monitoring and FinOps
Supporting capabilities
The work around the core offer.
The scope can extend into AI implementation, security testing, or automation when the operating boundary requires it.
AI implementation
Move a useful AI idea from prototype to an owned production system, with the integrations, controls, evaluation and handover it needs to survive real use.
AI security
Assess and improve the security of AI-enabled applications across prompts, data, APIs, identities, tools and cloud infrastructure—not only the model endpoint.
Governance support
Translate AI policy and risk expectations into inventories, decision records, controls, evidence and operating responsibilities a team can actually maintain.
Automation
Replace brittle manual handoffs with observable workflows that connect the tools a team already uses, preserve approvals and handle exceptions deliberately.
IT and cloud
Create the access, deployment, monitoring, backup and support layer that lets a small team run modern software and AI services without avoidable operational fragility.
Selected work
What the work looks like when the problem is real.
The difficult part is rarely choosing a tool. It is deciding what the system must do, how the team will know it works, and what happens when it does not.
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What turns access into capability
A platform or credit balance is not the operating system.
Each core offer combines the commercial resource with the technical work that makes it measurable, controlled and useful in production.
Demand planning
Forecast demand before committing the headroom.
01Evaluation and observabilityCommercial resourceObservability environment, traces and evaluation dataOperating work around itInstrumentation, representative datasets, evaluators, release gates and review ownership02Model inference capacityCommercial resourceAnnual API, inference and embedding capacityOperating work around itWorkload forecast, integration, provider configuration, quotas, rate limits and usage optimisation03Cloud capacity programmesCommercial resourceAnnual compute, GPU, storage, database and managed-service capacityOperating work around itArchitecture, provisioning, identity, monitoring, backup, cost allocation and FinOps reviews
- 01
Frame
Connect the resource to the product decision.
- 02
Provision
Integrate the platform, API or cloud environment.
- 03
Control
Set evidence, quotas, access, alerts and ownership.
- 04
Review
Use real operation to guide changes and renewal.
Ways to start
A scope you can understand before the work begins.
These are engagement shapes, not packaged theatre. The final scope names the system boundary, assumptions, client inputs, outputs, change points and owner.
Assessment
Find the real boundary before committing to a build.
Useful when the architecture, risk, quality baseline or right intervention is still unclear.
Typical start: 2–5 weeks
Leaves behind: System map, evidence, prioritised findings and a buildable next-step plan.
Implementation
Deliver one defined system through release and ownership.
Useful when the outcome is understood and the team needs accountable technical delivery.
Typical start: 6–12 weeks
Leaves behind: Working implementation, controls, evaluation, documentation and handover.
Embedded improvement
Improve a live system against an agreed operating standard.
Useful when an internal team owns the product but needs senior capacity across AI, security or operations.
Typical start: monthly
Leaves behind: A visible backlog, shipped improvements, updated evidence and stronger internal ownership.
The delivery method
Three moves. Clear ownership.
The work can be an assessment, a build, or both. What stays constant is the path from constraint to evidence to handover.
- 01
Frame the decision
Map the system, the risk, the owner and what evidence would change the next decision.
- 02
Build the operating layer
Implement the pipeline, control, integration, evaluation or remediation against the real environment.
- 03
Leave a system behind
Document limits, transfer ownership, train the team and make the next actions explicit.
Working fit
Small team. Serious operating standard.
Estma is designed for startups and SMBs that need senior technical work without a large consulting layer around it.
How we workA good fit
- There is a real product, workflow or operating decision.
- An accountable owner can review scope and evidence.
- The team will provide access to the system and its context.
- The goal is a maintainable capability, not a permanent dependency.
Probably not a fit
- A strategy deck is required without implementation or ownership.
- A predetermined tool must be installed regardless of the problem.
- Security testing is requested without written authorisation.
- A guaranteed AI outcome is expected without representative evidence.
Before we talk.
The practical questions most teams need answered before deciding whether to start.
What happens on the first call?
We establish the decision you need to make, what exists today, the people and systems involved, the main constraints, and whether Estma is a sensible fit. You do not need a polished brief.
Do you work with an internal team or deliver independently?
Both. Most engagements pair one accountable Estma lead with the people who understand the product, workflow and operating environment. The division of work is made explicit in the scope.
Can we start with an assessment before committing to a build?
Yes. A short assessment is often the best starting point when the system boundary, risk or right implementation path is still unclear. Its output is useful even if a different team performs the build.
How do you price the work?
Defined assessments and builds are normally fixed around an agreed scope and assumptions. Embedded improvement and operational support use a monthly capacity model. We identify likely change points before work starts.
Can model or cloud capacity be part of the engagement?
Yes. Where it fits the workload and commercial terms, Estma can scope annual inference or cloud capacity together with forecasting, provisioning, controls, monitoring and optimisation. The final offer states the provider, resource boundary, term and exclusions.
What do you need from our team?
An owner who can make decisions, access to the relevant systems and people, and timely review of work that changes the product or risk posture. We do not need a large steering committee.
What do you not do?
We do not sell AI strategy disconnected from implementation, promise accuracy without representative evaluation, perform testing without authorisation, or present operational governance as formal legal advice.
What needs to work better?
A rough brief is enough. The first conversation is about fit, access, risk and who owns the result.