We're not a badge wall. We ship on whatever your stack demands.
Most consultancies list partner logos. We list the platforms our senior practitioners have actually shipped production AI on — inside a Fortune-100 retailer, a Top-3 consulting firm, and venture-backed AI startups. We hold no vendor quotas and take no referral margins, so our architecture recommendations answer to your P&L, not a partner tier.
Vendor-neutral by design
We are deliberately unaffiliated. When we recommend Snowflake over Databricks — or the reverse — the only thing it optimizes is your workload.
Application & agents
copilots · RAG apps · straight-through processing
AI & LLM layer
Claude · GPT · LangGraph — reasoning & orchestration
Cloud & AI platforms
AWS · Google Cloud · Azure — training, serving, scale
Data platforms
Snowflake · Databricks · dbt — the governed foundation
Reference architecture. The governed data foundation AI systems live or die on — vendor-neutral, audit-ready.
Category 1 — Data platforms
Snowflakewordmark
Governed data foundations, Snowpark ML pipelines, Cortex LLM functions — including Guidewire CDA-to-Snowflake patterns for insurance and HIPAA-aligned clinical models for healthcare.
Databrickswordmark
Lakehouse on Delta Lake, Spark-scale feature engineering, MLflow-governed lifecycle, Unity Catalog, Mosaic AI. Our default for high-volume ML.
dbtlogo ✓ OSS
Transformation layer and semantic contracts on Snowflake / Databricks.
Category 2 — Cloud & AI platforms
AWSwordmark · strict
SageMaker training/serving, Bedrock for managed LLM access, and horizontally scalable data + ML platforms.
Google Cloudwordmark · strict
Vertex AI pipelines and model serving — where our team shipped patient triage at 85% recall inside clinical workflows.
Microsoft Azurewordmark · strict
Azure ML and Azure OpenAI Service — the pragmatic choice for Microsoft estates and HIPAA-aligned GenAI.
Category 3 — AI & LLM layer
Anthropic Claudelogo ✓
Long-context reasoning for document-heavy workflows — submissions, prior auth, MLR review — with agentic tool use under human-in-the-loop scaffolding.
OpenAIlogo ✓
GPT-class models for generation-heavy workloads and structured extraction at scale.
LangChain / LangGraphlogo ✓ OSS
Orchestration for RAG pipelines and multi-agent systems — the framework behind the intake and recommendation systems our team has shipped.
Open modelsLlama · Mistral
Self-hosted inference where data residency, cost-at-scale, or regulator posture rules out API-only models.
Category 4 — Data & MLOps tooling
MLflowlogo ✓ OSS
Experiment tracking, model registry, and the audit trail MRM actually requires.
Airflowlogo ✓ OSS
Scheduled and event-driven pipeline orchestration for data and retraining workflows.
Vector DBspgvector · Pinecone · Weaviate
Retrieval layers for RAG — chosen per latency, scale, and governance need.
Feature storesFeast · Tecton
Online/offline feature consistency — the piece that breaks most pilot-to-prod transitions.
Evals & monitoringLangSmith · Ragas · Arize
The Run & Optimize toolchain — evals, drift detection, model monitoring.
GovernanceNIST AI RMF
MRM and audit-readiness aligned to NIST AI RMF and sector bulletins.
One stack, three service lines
Strategy & Advisory chooses from this menu with no vendor thumb on the scale. Build & Engineering ships on it. Run & Optimize keeps it healthy — evals, monitoring, retraining, cost control.
All product names, logos, and brands are property of their respective owners and are used for identification purposes only. Their use does not imply endorsement, sponsorship, or a formal partnership. Statistiq holds no vendor partnerships and is vendor-neutral by design. Real logos shown where terms permit (Anthropic, OpenAI, OSS projects); strict vendors rendered as wordmarks — verify each vendor's current brand terms before shipping any logo file.
Not sure which stack your problem needs?
Tell us the workload. We'll tell you what we'd build it on — and why — with no partner tier on the scale.