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Representative case studies

Systems built
for reality.

Detailed solution blueprints showing how Claspio connects product thinking, data, AI, software, and cloud engineering around a business problem.

About these studies

These are representative engagement patterns. Client identities, proprietary data, and production metrics are intentionally omitted; the architecture and delivery decisions reflect the systems we are equipped to build.

01 / AI + Knowledge

Operations copilot

Grounded answers and workflow automation over trusted company knowledge.

View architecture ↘ 02 / Data + Analytics

Data control tower

A governed real-time platform for operational visibility and decision support.

View architecture ↘ 03 / Product + Cloud

Product platform

A resilient multi-tenant foundation for faster, safer product delivery.

View architecture ↘
Case / 01
Generative AIData platformCloud
Knowledge operations

An AI copilot
teams can trust.

A governed assistant that finds evidence, respects access controls, answers with citations, and triggers approved operational workflows.

01 / Situation

Knowledge existed. Access did not.

Policies, tickets, product documentation, and operational history lived across disconnected systems. Teams lost time searching, repeated work, and relied on answers with unclear provenance.

02 / Response

Ground every answer.

We designed a permission-aware retrieval layer, an evaluated LLM gateway, and workflow actions that remain observable and reversible.

03 / Value pattern

Faster work, visible trust.

Answers carry evidence, quality is measured continuously, knowledge updates without an application release, and risky actions remain under human control.

Reference architecture / Operations copilotRAG + Actions + Evaluation
Sources
Policies
Support tickets
Product data
→
Ingestion
Connectors
Parsing + chunking
Metadata + ACL
→
Knowledge
Object storage
Vector index
Hybrid retrieval
→
Intelligence
LLM gateway
RAG orchestration
Evals + guardrails
→
Experience
Team copilot
Workflow actions
Quality analytics
Cross-cutting controlsIdentity & permissionsEncryptionPrompt and response tracingHuman approvalCost controls
Delivery scope
  • Knowledge-source audit and retrieval-quality baseline
  • Permission-aware ingestion and indexing pipelines
  • Model routing, prompt management, and response citations
  • Offline evaluation suite and production feedback loop
  • Workflow integrations with approval and rollback paths
Key architecture decisions
  • Separate knowledge ingestion from the user-facing application
  • Keep model providers replaceable behind a controlled gateway
  • Apply source permissions before retrieval, not after generation
  • Treat evaluation data as a versioned production asset
  • Require explicit approval for high-impact workflow actions
Case / 02
Data engineeringAnalyticsMachine learning
Operational intelligence

One reliable view
of what is happening.

A real-time control tower that turns fragmented operational events into governed metrics, alerts, forecasts, and decision-ready workflows.

01 / Situation

Every team had a different truth.

Transactional systems, partner feeds, spreadsheets, and product telemetry produced delayed and conflicting operational views. Teams reacted after issues became visible to customers.

02 / Response

Design for events and history.

We shaped a platform where streaming and batch data share quality rules, lineage, semantic definitions, and a consistent serving layer.

03 / Value pattern

Decisions move closer to reality.

Operational teams work from governed metrics, anomalies surface earlier, analysts trace every number, and models use the same trusted foundation as reporting.

Reference architecture / Data control towerStreaming + Lakehouse + Semantic layer
Producers
Core systems
Partner APIs
Product events
→
Movement
CDC pipelines
Event streaming
Batch orchestration
→
Lakehouse
Raw + curated
Quality contracts
Catalog + lineage
→
Intelligence
Semantic models
Feature pipelines
Anomaly detection
→
Decisions
Control tower
Alerts + workflows
Forecast APIs
Platform foundationData contractsRole-based accessObservabilityLineageRetention policy
Delivery scope
  • Source-system mapping and critical metric definition
  • Streaming, CDC, and batch ingestion pipelines
  • Lakehouse layers with automated quality contracts
  • Governed semantic layer for analytics and applications
  • Operational dashboards, alerts, and forecasting interfaces
Key architecture decisions
  • Preserve immutable raw events for replay and auditability
  • Define ownership and quality expectations at the data-product boundary
  • Keep business definitions in a governed semantic layer
  • Unify platform observability with data-quality monitoring
  • Serve analytics and machine learning from consistent curated data
Case / 03
Product engineeringCloud platformDevOps
Platform modernization

A product foundation
built to evolve.

A secure multi-tenant platform that separates product experiences from core services and gives teams a dependable path from code to production.

01 / Situation

Every release carried too much risk.

A tightly coupled application made changes slow, tenant behavior inconsistent, and production issues difficult to isolate. Infrastructure knowledge lived with too few people.

02 / Response

Modernize by boundary.

We designed an incremental platform around stable APIs, domain services, events, automated delivery, and a shared observability model.

03 / Value pattern

Safer change becomes routine.

Teams deploy independently, platform controls are reusable, failures are easier to contain, and new product surfaces build on the same dependable core.

Reference architecture / Product platformMulti-tenant + Event-driven + Cloud-native
Experiences
Web application
Mobile clients
Partner portal
→
Edge
CDN + WAF
API gateway
Identity
→
Platform
Domain services
Workflow engine
Tenant controls
→
State + Events
Transactional data
Event bus
Search + cache
→
Runtime
Managed containers
CI/CD + IaC
Observability
Engineering controlsTenant isolationSecrets managementPolicy as codeSLOs + tracingAutomated rollback
Delivery scope
  • Domain mapping and modernization sequence
  • API, event, and tenant-isolation standards
  • Shared cloud landing zone and infrastructure modules
  • Automated testing, delivery, and environment promotion
  • Service-level objectives, tracing, dashboards, and runbooks
Key architecture decisions
  • Use incremental replacement instead of a high-risk rewrite
  • Separate tenant policy from domain implementation
  • Adopt events where teams need loose coupling, not everywhere
  • Provide paved-road deployment patterns through the platform
  • Make reliability targets visible alongside product metrics
Your system is different

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