Use-case and risk brief
Users, task, exclusions, data sources, quality bar, sensitive-data constraints, failure handling, and accountability.
Harmita services
AI integration.
Start with a bounded use case.
AI is most useful when the task, source material, failure conditions, and reviewer are explicit. We help teams prototype and integrate a defined workflow without presenting generated output as infallible.
Scoped to your project
A defined outcome.
Reviewable work.
- One task and success condition first
- Evaluation with representative cases
- Human review and limits documented
Final deliverables, schedule, fees, and responsibilities are confirmed in a proposal.
Where it fits
Useful when the problem is clear.
These are common starting points, not a qualification checklist. Discovery confirms whether this service is a sensible fit.
- A team evaluating an assistant for a repeatable internal task
- A website that needs search across approved documents
- A product adding a narrow drafting, classification, or extraction feature
- An operations team exploring automation with human review
Problems to solve
Start with the friction, not a package.
A useful brief names what is failing today and what a better operating state would look like.
- An AI idea is broad but the business decision is unclear
- Source information is scattered or inconsistently maintained
- Generated answers cannot be traced or safely reviewed
- Provider costs, privacy, latency, and failure cases are unknown
Possible deliverables
Concrete work you can review.
The proposal selects and adapts the deliverables needed for your objective. Items not named in the proposal are not implied.
Provider and architecture options
A comparison based on capability, integration surface, cost model, data terms, and operational fit.
Working prototype
A narrow experience for the approved workflow, connected to representative sources or inputs.
Evaluation set and findings
Representative cases, expected behaviour, observed limits, and a decision about whether to progress.
Integration and operating notes
Access controls, prompting or retrieval logic, review path, error states, usage monitoring, and handover.
How the work moves
Decisions and inputs are visible.
Each stage names what Harmita does and what we need from your team, so dependencies do not disappear inside a timeline.
- 01
Bound the use case
Define the user, input, expected output, prohibited use, escalation path, and quality threshold.
Needed from you: Representative tasks, approved data, sensitive-data rules, and the accountable person.
- 02
Test feasibility
Compare options and run a small prototype before committing to a larger build.
Needed from you: Feedback on accuracy, usefulness, tone, and unacceptable failure modes.
- 03
Integrate the approved workflow
Connect the approach to the agreed product or process with visible loading, failure, and review states.
Needed from you: System access, approved provider accounts, security requirements, and an implementation owner.
- 04
Evaluate and hand over
Rerun agreed cases, record known limits, and document monitoring and change responsibilities.
Needed from you: Acceptance decisions, operational owners, and a plan to keep sources current.
Quote factors
What shapes scope and fees.
Fees are quoted after the scope is understood. The factors below usually change the effort, dependencies, and delivery plan.
- Number and complexity of AI tasks
- Source-data readiness and access controls
- Evaluation depth and required accuracy
- Application and provider integrations
- Usage volume, latency, and monitoring needs
Boundaries and dependencies
Clear before work begins.
- Generative systems can return incorrect, incomplete, biased, or inconsistent output; review controls remain necessary.
- Provider usage fees, rate limits, model changes, availability, and data terms are external dependencies.
- High-impact decisions require specialist governance beyond a general integration scope.
Questions, answered
A few things you might ask.
Questions about ai integration, project scope, dependencies, and working with Harmita.
Can AI use our own documents?
Potentially. Documents need to be approved, accessible, current, and suitable for the intended audience. We also define source visibility and missing-evidence behaviour.
How do you choose a provider?
We compare the task, quality bar, integration options, data terms, cost model, latency, and operational constraints—not popularity alone.
Can the output be fully automated?
That depends on consequence and tolerance for error. Many workflows should retain review, approval, or escalation.
What does a prototype prove?
It shows how a narrow approach behaves on representative cases. It does not prove universal accuracy, future provider behaviour, or production readiness.
Useful next reading
Connect this work to the wider system.
Continue with a related service, practical guide, or browser-based tool.
A sensible next step