AI opportunities exist, but the priority is unclear
Compare use-case value, model fit, readiness, risk, and the smallest responsible next scope.
Vendor-neutral AI decision, implementation, and operations
Decide where AI creates value. Move the right workflows into production.
Datrick helps technology leaders prioritize AI use cases, select the right models, validate real workflow value, and build the controls required for dependable production use.
Official Anthropic Partner
Confidential leading-model evaluation experience
US-incorporated · Senior-led · Global delivery
- Decide
- use-case value, model fit, readiness, risk, roadmap
- Implement
- workflow integration, evaluation, human control, rollout
- Operate
- monitoring, incidents, change control, continuous improvement
AI delivery model
Vendor-neutral
Decision
Value and model fit
Prioritize use cases and compare suitable model routes against real constraints
Evidence
Pilot the workflow
Test quality, operating value, failure modes, integration, and total cost
Production
Controls and ownership
Implement access, evaluation, logging, human review, fallback, and support
Foundation
Data and systems
Production experience across databases, migration, BI, analytics, and operations
When teams bring us in
Senior judgment when an AI investment or critical system needs a responsible owner.
A promising AI prototype needs to survive real operations
Add evaluation, integration, access control, human review, observability, fallback, and ownership.
A critical data or AI system loses its technical owner
Recover access, establish the baseline, stabilize operations, and create a documented ownership path.
An IT service firm needs senior AI and data delivery
Add accountable specialist delivery under your brand while retaining the client relationship.
Delivery capabilities
One path from AI investment decision to dependable operations.
AI readiness and model selection
Use-case value, model routes, data and integration readiness, governance, risk, and an evidence-backed pilot roadmap.
AI implementation and evaluation
Representative evaluations, workflow integration, human review, production controls, rollout, and operating guidance.
Production data and systems foundation
Database operations, migration, BI, analytics, observability, documentation, and continuity behind dependable AI delivery.
Datrick AI Decision-to-Operations Framework
Six decision gates from opportunity to dependable operations.
Each gate resolves a specific executive or operating question, produces reviewable evidence, and ends with an explicit decision before more investment is committed.
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01
Value
Where can AI create measurable value?
Prioritize workflows against baseline effort, quality, capacity, revenue, risk, and adoption.
Evidence: use-case and ROI scorecard -
02
Model fit
Which technical route fits the work?
Compare model and automation options using representative tasks, constraints, and total operating cost.
Evidence: model selection record -
03
Readiness and risk
What must be ready before delivery?
Expose gaps across data, integration, security, governance, ownership, evaluation, and adoption.
Evidence: readiness and risk register -
04
Pilot evidence
Does the workflow work under realistic conditions?
Test bounded scope, representative scenarios, users, failure modes, controls, and acceptance criteria.
Evidence: pilot decision package -
05
Production controls
Can the workflow operate safely and reliably?
Add access control, observability, human review, fallback, auditability, documentation, and ownership.
Evidence: production readiness record -
06
Managed operations
How will quality and value hold over time?
Monitor service health, model behavior, cost, incidents, data dependencies, adoption, and improvement work.
Evidence: operating baseline and cadence
Institutional trust register
Four claims a buyer can trace before starting a conversation.
Datrick separates company identity, ecosystem position, named accountability, and delivery evidence so each can be reviewed on its own terms.
- Corporate identity
- Datrick, Inc. Delaware corporation with operating presence in Wilmington and Istanbul. Review company structure
- Named accountability
- Can Goktug Ozdem Founder and senior technical lead responsible for scoping, delivery standards, and technical review. Review leadership
- Ecosystem position
- Official Anthropic Partner Claude implementation and enablement connected to production data, controls, and operating ownership. Review Claude capability
- Evidence standard
- Claims stay bounded Measured outcomes, operating outcomes, and confidential delivery are labeled separately. Review delivery evidence
AI engagement paths
Choose the smallest engagement that resolves the next decision.
Each path has a defined decision, evidence standard, and commercial boundary. Start where uncertainty is highest, then expand only when the evidence supports it.
AI Decision Sprint
For one priority workflow when the value case, model route, readiness, or responsible pilot scope is still unclear.
- Produces
- Value case, model evidence, risk register, and pilot charter
- Investment
- $15,000 fixed scope
- Typical duration
- 2 weeks
Production AI Pilot
For a bounded workflow that must prove quality, integration, controls, user fit, and operating viability under realistic conditions.
- Produces
- Working pilot, evaluation baseline, controls, and decision package
- Investment
- Scoped to workflow and evidence requirements
- Boundary
- Defined acceptance criteria before implementation
Managed AI Operations
For production AI workflows that need accountable ownership across quality, incidents, model changes, cost, releases, and improvement work.
- Produces
- Operating baseline, service cadence, reporting, and prioritized backlog
- Investment
- From $15,000 per month
- Boundary
- Ownership and service scope agreed before transition
AI delivery
Official Anthropic Partner
From expert model evaluation to governed Claude workflows.
Datrick combines production data experience with confidential leading-model training work. We help AI teams improve technical quality and help enterprises put Claude into controlled operations.
If the use case or platform is still unclear, start with a vendor-neutral AI readiness assessment and model selection before funding implementation.
Technical model evaluation
Coding, SQL, data, analytics, rubrics, reference answers, and model-output review.
Claude workflow implementation
Internal assistants, document intelligence, data copilots, reporting, and operational automation.
Governance and production rollout
Evaluations, access controls, logging, human review, fallbacks, documentation, and support.
Engagement model
Structured enough for enterprise buyers, lightweight enough to start quickly.
-
1
Written scoping
Share the situation, systems, risks, prior attempts, and timeline.
-
2
Delivery plan
Define ownership, access, deliverables, escalation, and acceptance.
-
3
Senior delivery
Execute through a named lead with visible status, findings, and decisions.
-
4
Continuity
Document the work, transfer knowledge, and define ongoing support where useful.
Evidence before investment
What a scoped engagement is meant to change.
The right starting point should produce evidence, reduce operating risk, and make the next decision easier. These are verified examples, published only at the level confidentiality allows.
Review the evidence standard and anonymized cases
Reporting warehouse
68% faster queries
- SituationSlow, scan-heavy reporting workloads.
- DeliveryQuery, model, and scan-behavior optimization.
- Outcome68% faster queries and 85% lower scan volume.
Recurring partner delivery
Initial delivery expanded into 5+ years of work
- SituationAn IT service firm needed dependable delivery behind its client relationship.
- DeliveryDBA/NOC, migration, BI, reporting, and analytics support.
- OutcomeReliable delivery led to repeat work across additional services.
Confidential AI delivery
Expert review for leading AI model programs
- SituationTechnical evaluation required judgment across code and data.
- DeliveryRubrics, reference answers, SQL, analytics, and model-output review.
- OutcomeRepeatable confidential delivery across technical disciplines.
Contact
Start with the smallest useful step.
Describe what is happening, what is at risk, and what has already been tried. A senior lead will review it and recommend the right next step within one business day.
Planning ranges
A useful starting point, not a rigid package.
Every situation is different. These ranges are here to help with early planning; after review, we recommend the smallest scope that can produce a useful outcome.
- AI Decision Sprint
- $15,000 fixed scope
- Production AI Pilot
- Scoped to evidence required
- Managed AI Operations
- From $15,000/month
Urgent support is prioritized after written intake. Final pricing reflects risk, access, response time, and delivery ownership.