Executive clarity
One-page decision logs, investment phasing, and risk registers aligned to AI use cases — updated as you learn, not abandoned after a deck.
Technology consulting
Align your data so AI ships with confidence.
We partner with leaders to design data foundations, governance, and delivery patterns so models, agents, and analytics run on evidence your teams can defend — in the boardroom and in production.
- Strategy through implementation
- Vendor-neutral architecture
- Knowledge transfer built in
Data first
AI success is decided before the first model train.
Faster reviews
Risk, legal, and security get answers they can audit.
Operational clarity
Runbooks your platform team can own after we leave.
Why data alignment matters now
Generative AI and agentic workflows amplified a long-standing truth: if definitions drift, access is opaque, or lineage stops at a spreadsheet, initiatives stall — or ship with silent risk.
simplific.ai helps you connect business definitions to technical contracts, pipelines to policies, and prototypes to the controls your enterprise already expects. The result is speed with accountability, not despite it.
- Executive-ready roadmaps tied to measurable data maturity milestones
- Patterns for hybrid cloud, SaaS, and legacy sources common in regulated environments
- Hands-on workshops so data, ML, and application teams share one vocabulary
Services
Modular engagements across assessment, architecture, and enablement — scoped to your timeline and internal capacity.
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01
AI & data readiness
Current-state inventory, gap analysis, and a sequenced plan that links data work to model or agent outcomes executives care about.
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02
Data quality for ML
Profiling, validation, anomaly detection, and SLAs so training, fine-tuning, and inference stay aligned as upstream systems change.
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03
Governance & lineage
Policies, stewardship models, catalogs, and lineage graphs that satisfy audit while preserving builder velocity.
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04
Feature & training data design
Feature contracts, stores, reproducible snapshots, and testing hooks so experiments are comparable and promotable.
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05
RAG & knowledge corpora
Ingestion, chunking, embedding strategy, retrieval metrics, and citation hygiene for assistants grounded in your real documents.
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06
Vector & document architecture
Selection and integration of vector stores, hybrid search, sync jobs, and lifecycle management matched to scale and residency requirements.
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07
Privacy-preserving data for AI
Data minimization, tokenization, synthetic generation where appropriate, and access patterns that reduce exposure without blocking innovation.
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08
ML data ops & drift
Monitoring, feedback capture, retraining triggers, and incident response so drift is a managed signal — not a customer complaint.
Outcomes we drive
Concrete deliverables and decision rights — so initiatives progress when stakeholders are busy and priorities shift.
Engineering traction
Reference pipelines, IaC snippets, and test harnesses your teams can extend — avoiding “consultant-only” tooling lock-in.
Compliance-ready evidence
Traceability from prompt or prediction back to source systems, owners, and change history — structured for security and legal review.
Who we work with
Mid-market and enterprise teams modernizing analytics, launching copilots, or hardening existing ML — especially when multiple business units depend on the same data products.
We are comfortable alongside cloud partners, systems integrators, and internal platform groups. Our role is to tighten the data thread across those parties, not to replace them.
Financial services
Healthcare & life sciences
Manufacturing & logistics
Technology & media
Public sector
How we work
Engagements begin with a focused discovery — interviews, system walk-throughs, and artifact review — so recommendations reflect how work actually happens, not an idealized diagram.
We prefer thin slices that prove value early: a single high-value workflow, a bounded corpus, or one critical model path. Then we scale patterns and ownership across teams.
- Discover & map Stakeholders, systems, data contracts, and AI touchpoints — documented in shared tools.
- Design & align Target architecture, standards, and RACI so engineering and governance move together.
- Pilot & measure Quality bars, dashboards, and review cadences that make “good enough” objective.
- Scale & handoff Automation, training, and runbooks — we succeed when you do not need us for every change.
Capabilities we routinely touch
Representative stacks — we recommend based on your constraints, not a single vendor agenda.
Platforms & runtimes
AWS, Azure, GCP; Databricks, Snowflake; Kubernetes; major feature store offerings.
Data movement & quality
dbt, Airflow / Dagster, streaming buses, Great Expectations–class checks, observability hooks.
ML & AI patterns
Batch & online inference, RAG, vector DBs, model registries, evaluation harnesses.
Governance tooling
Data catalogs, lineage, IAM integration, privacy workflows, retention and classification.
Frequently asked questions
Practical answers for teams evaluating a data-focused AI partner.
Our core expertise is the data layer, governance, and delivery patterns around AI. We collaborate with your ML engineers or partners on modeling where needed, but we do not position ourselves as a model shop. That focus keeps engagements scoped and durable.
Readiness assessments often run four to eight weeks. Architecture and pilot workstreams commonly span eight to sixteen weeks depending on scope and access. We will propose a timeline after discovery — no open-ended retainers unless you explicitly want one.
Yes. Mutual NDAs and a short scoping call are standard. We treat your architecture and data sensitivities as confidential from first contact.
A short list of priority AI use cases, known data sources, current pain points (quality, latency, access), and any compliance boundaries. Existing diagrams or pipeline docs are helpful but not required — we can help you assemble them.
Contact us
Share your context, timeline, and what “success” looks like for your stakeholders. We respond within two business days with next steps or clarifying questions.
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