Bronze
Land as it arrived
Immutable or append-only landing from sources. Schema-on-read where needed, full audit trail, minimal transformation. The goal is recoverability and replay when upstream changes.
Data for AI
Having Data is ≠ Having
AI-Ready Data
Your data may be scattered across entities, locked inside transactional systems, or missing the domain-specific structure that AI demands. We prepare it through governed Medallion architectures and an agentic framework that accelerates the journey from raw enterprise data to AI-ready foundations.
If the warehouse, the API, and the slide deck disagree, every downstream system inherits the conflict. We engineer that away on purpose.
20+
ERPs Unified in Single Platform
97%
Validation Time Savings
200M+
Daily Data Points Processed
Sound Familiar?
Why Data Readiness Comes First
Enterprise AI initiatives often stall, not from lack of ambition, but from data that is not ready. Three patterns emerge again and again.
Grew Through Acquisition or Expansion
M&A, regional growth, or decentralized operations left you with 5, 10, 20 source systems, each with its own schema, its own definitions, and its own version of the truth. Leadership asks a question and gets three different answers.
Multi-Entity FragmentationERP Records Everything, Nobody Can Use It
Oracle EBS captures every transaction perfectly. But finance teams have built shadow Excel layers because the ERP was never designed for analytics. Month-end is a reconciliation crisis, not a moment of insight.
Transactional Data TrapDomain Data Complexity at Scale
Product taxonomies across 1,000+ retailers. Regulatory vocabularies for FDA submissions. Content standards across markets and languages. Generic data tools cannot handle semantics that require domain expertise.
Domain-Specific Structure GapMap the thread
The data engineering spine
Discovery, ingestion, modeling, platform fit, and governance are one thread. Treating them as separate projects is how lineage breaks and AI projects stall.
Start with visibility, end with something operators can trust.
- Map first: sources, dependencies, and contracts before tooling debates.
- Land reliably: batch, CDC, or streaming paths with explicit schemas and tests, chosen by latency targets, source constraints, and operational needs.
- Model once: shared entities and metrics—not one-off extracts per team.
- Fit the platform: Snowflake, Databricks, Microsoft Fabric, AWS (e.g. S3, Glue, Redshift), Azure (e.g. Synapse, Data Factory, ADLS)—workload fit, cost, performance, operational overhead, and residency/compliance realities, not vendor slides alone.
- Govern in the pipeline: quality and ownership where data moves, not only in decks.
A governed semantic layer keeps metrics and definitions consistent across BI, APIs, and AI use cases.
Architect takeaway: A foundation is not “we bought Snowflake or Databricks.” It is the rules and automation that keep definitions, lineage, and access coherent—on whichever cloud data platform or lakehouse you standardize on.
Discovery pilot Prove the engineering spine on one path · expand
Pick a bounded slice—discovery through governance—with clear success checks. We time-box scope so you see delivery mechanics and ownership before wider funding.
Layer the lakehouse
Medallion architecture: layers with a job each
Bronze, silver, and gold are not vanity labels. They are separation of concerns: land raw, conform truth, publish meaning.
- Bronze: immutable landing, replay when upstream changes.
- Silver: one set of keys and dimensions across feeds.
- Gold: certified metrics and interfaces for BI, APIs, and features.
Skipping layers to move faster usually means debugging in production. Medallion patterns are implemented differently across platforms—more natively in lakehouse environments such as Databricks and Fabric, and logically modeled in warehouse platforms such as Snowflake. The discipline still carries across native AWS / Azure services.
Silver
Conform and cleanse
Standardized types, keys, deduplication, and shared dimensions. This is where technical debt is paid: one way to represent a customer, a product, a region across feeds.
Gold
Curated for use
Subject-mart friendly models, certified metrics, and interfaces tuned for BI, APIs, and model features. Published only when ownership and tests say it is safe.
Discovery pilot Prove one medallion promotion with tests · expand
Time-box Bronze→Silver or Silver→Gold for a single family with lineage and automated checks—so stakeholders see how layers behave before you scale spend.
Encode relationships
Knowledge graph: relationships without losing the warehouse
Tables remain central for facts, history, and metrics. Graphs are valuable when the problem is inherently relational: who owns what, what depends on what, which policy applies to which entity set.
Graph complements the warehouse:
- 360 views across silos (party, product, asset, policy).
- Permission and policy reasoning over relationships.
- Context bundles for retrieval workflows where brittle mega-joins hurt.
Practical note: Graph is not a default layer for every estate; it pays off when questions are inherently relational and encoding those edges only in SQL becomes a maintenance tax. We align graph schemas with your semantic layer so metrics and edges do not diverge.
Discovery pilot Pilot graph + warehouse alignment · expand
Scope a bounded entity model and edges tied to your semantic layer—enough to validate query patterns and ownership, not a boil-the-ocean graph program.
AI in data engineering
AI Agents in Data Engineering solutions
Five Optisol data agents accelerate extraction, modeling, migration, quality, and analysis—schema inference, SQL/transformation draft generation, anomaly detection, migration/parity checks, and join-health or freshness profiling—always as proposals and checks inside your tests, contracts, and approvals.
- Each agent has a narrow job; together they cover the pipeline without replacing engineering judgment.
- Production changes still flow through your governance: no silent deploys from automation.
Data Extraction Agent
Maps candidate sources, prepares repeatable ingestion patterns, and flags scope and security boundaries before anything lands in Bronze.
Data Modeling Agent
Proposes keys, types, and conformed shapes aligned to your semantic rules—output is reviewed, not auto-merged to production.
Data Migration Agent
Supports path design, script drafts, and parity checks across environments so cutover risk is visible before go-live.
Data Quality Agent
Surfaces anomalies and drift against your declared rules; routes signals to data owners instead of silent fixes.
Data Analysis Agent
Accelerates profiling of freshness, cardinality, and join health so teams prioritize what actually blocks consumers.
Discovery pilot Agent-assisted discovery on your inventory · expand
Compress profiling and contract drafting for one domain—always inside your review gates—so you see velocity without bypassing governance.
Cloud data platforms & analytics estates
Full delivery
End-to-end data engineering programs—medallion architecture, pipelines, semantic layer, and governance—built and handed over on the platform you already run.
Discovery pilot Not sure which platform fits your workload? · expand
We assess workload fit, cost, operational overhead, and residency constraints across Snowflake, Databricks, Microsoft Fabric, AWS, and Azure—then recommend based on your estate, not vendor slides. Share your platform and constraints and we will respond within one business day.
Resource augmentation
Embed certified Data Engineering specialists directly into your team on Snowflake, Databricks, Microsoft Fabric, AWS, or Azure—scoped to your sprint cycle and governance gates.
Resource augmentation Need a Data Engineering expert on your platform team? · expand
We embed experienced Data Engineers with hands-on platform depth directly into your team—Snowflake, Databricks, Microsoft Fabric, AWS (S3, Glue, Redshift), and Azure (Synapse, Data Factory, ADLS). They work inside your sprint cycle, your tools, and your governance gates—not as a black box offshore team.
Tell us your platform, the gap in your team, and your timeline. We respond within one business day.
Whichever mode fits—full program or expert augmentation—work runs on the stack you already use, aligned to residency, cost, and operational reality.
Why Optisol
Engineering-led, outcome-accountable
You get builders who own the thread from source systems to certified metrics—not a strategy deck that stalls when it meets your ERP, cloud warehouse, or compliance boundary.
Shipped, not theorized Our leads have delivered lakehouse and migration programs in regulated, messy estates—not slideware-only architecture.
One accountable thread Discovery, modeling, platform fit, and handover stay with a coherent team so lineage and definitions do not fracture at org seams.
Artifacts you operate Contracts, tests, lineage views, and runbooks transfer to your platform owners; we stay on agreed escalation paths.
Proof before scale Bounded pilots when you need to validate delivery mechanics, access, and success criteria before wider funding.
Security and privacy by design Access, residency, and change windows are scoped from week one—not bolted on before launch.
Global execution USA, UK, Australia, UAE, and India—delivery models matched to your stakeholders and hours.
Discovery pilot Talk through fit, scope, and constraints · expand
Share your estate and success criteria—we respond within one business day.
OUR RECENT WORKS
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