Full-stack capability, from strategy to production infrastructure
Twenty-two capabilities across five disciplines and fourteen named solutions — the depth required to take an AI initiative from first principles through architecture into a system running in production.
Twenty-two capabilities across five disciplines
AI Strategy & Governance
Enterprise AI Strategy
Build-vs-buy decisions, model and vendor evaluation, multi-year AI roadmaps, and the operating model needed to move initiatives from pilot to platform.
Responsible AI
Fairness, robustness, and human-in-the-loop review built into model lifecycles from data collection through production monitoring.
AI Governance
Model risk frameworks, approval workflows, and auditability standards that satisfy both regulators and engineering velocity.
Generative & Agentic AI
Generative AI
Production generative systems built on rigorous evaluation, not demos — from prompt architecture to fine-tuned, cost-optimized models.
LLM Applications
Enterprise applications powered by large language models, engineered for latency, cost, and reliability at production scale.
Agentic AI
Multi-agent orchestration, tool-use, and reasoning systems designed with the evaluation and guardrail infrastructure production autonomy requires.
RAG Systems
Retrieval-augmented systems over structured and unstructured enterprise data, tuned for grounding accuracy and freshness, not just recall.
Synthetic Data Generation
Chain-of-thought and multi-persona synthetic data pipelines that solve the data bottleneck behind every fine-tuning effort.
Annotation Platforms
Multi-agent labeling systems combining LLM annotators, classical ML, and human review with consensus scoring and confidence routing.
Machine Learning & MLOps
Machine Learning Platforms
Config-driven, multi-tenant ML platforms that turn bespoke model-training projects into repeatable infrastructure.
MLOps
Training, evaluation, deployment, and monitoring pipelines engineered for reproducibility and fast iteration, not one-off notebooks.
Knowledge Graphs
Entity and relationship models that give LLM and analytics systems structured, queryable context grounded in enterprise reality.
Vector Databases
Embedding infrastructure and retrieval tuning for semantic search, deduplication, and grounding at production query volume.
AI Product Engineering
Full-cycle product engineering for AI-native applications — from architecture through interface, shipped by engineers who own outcomes.
Data Platforms & Engineering
Data Platforms
Foundational data infrastructure engineered for the throughput, governance, and lineage that production AI depends on.
Modern Data Warehouses
Warehouse architecture and migration across Snowflake, BigQuery, and Databricks, built for cost-aware scale.
Feature Stores
Consistent, low-latency feature infrastructure that closes the gap between training and serving.
Cloud Data Engineering
Pipeline architecture across AWS, Azure, and GCP engineered for reliability, cost discipline, and auditability.
Analytics & Decision Intelligence
Enterprise Analytics
Analytics infrastructure and modeling that turns fragmented reporting into a single, trusted operating view.
Predictive Analytics
Forecasting and risk models built for the volatility of production data, validated against real business outcomes.
Decision Intelligence
Systems that connect data, models, and reasoning directly to decisions — not dashboards that require a human to close the loop.
Business Intelligence Modernization
Legacy BI replaced with governed, self-service, AI-augmented analytics that executive teams actually trust.
Platforms we've engineered, framed by the problem they solve
Every solution below started as a specific, hard problem inside a production environment. We productized the answer so it could be rebuilt for your data, your scale, and your constraints.
Synthetic Data Platform
- The problem
- Fine-tuning and evaluation both depend on labeled, realistic data at a volume manual curation cannot produce fast enough.
- Our approach
- A chain-of-thought generation pipeline with persona-based variation, multi-teacher grounding, and automated two-stage deduplication, gated by an LLM-as-jury quality bar before data ever reaches a training run.
- Outcome
- Tens of thousands of curated, deduplicated examples per run, cutting new-pipeline setup from weeks to hours and becoming the default data source for downstream fine-tuning.
Annotation Platform
- The problem
- Manual labeling is the slowest, most expensive step in building any supervised or fine-tuned model, and quality is inconsistent across annotators.
- Our approach
- Independent LLM policy-agent labelers, a judge model for consensus, and classical ML annotators combined under Krippendorff's alpha agreement scoring, with confidence-based routing between auto-accept, spot-check, and deep review.
- Outcome
- Large-scale labeling cycles compressed from weeks to hours, with the majority of items resolved automatically and only genuine edge cases reaching human reviewers.
Enterprise Knowledge Assistant
- The problem
- Institutional knowledge is scattered across documents, wikis, and structured systems that no single team can query coherently.
- Our approach
- Retrieval-augmented architecture unifying structured and unstructured sources behind a single grounded interface, with citation tracing and freshness controls.
- Outcome
- Real-time, trustworthy answers to operational questions that previously required locating the right person and the right document.
AI-Powered BI Assistant
- The problem
- Business intelligence tools answer questions only if someone already knows which dashboard, filter, and metric definition to use.
- Our approach
- Natural-language-to-SQL reasoning validated against governed metric definitions, with automated sanity checks before any number reaches a user.
- Outcome
- Analysts and executives query data directly in plain language, with the same governance guarantees as a certified dashboard.
Executive Insights Platform
- The problem
- Leadership teams operate on stale, manually assembled reporting that lags the decisions it's meant to inform.
- Our approach
- Real-time OKR and product-health surfaces built directly on production data, with automated anomaly flagging so leadership sees what changed, not just what happened.
- Outcome
- Executive and board-level visibility into performance that updates continuously instead of on a reporting calendar.
GenAI Analytics
- The problem
- Analytics teams spend more time assembling and validating data than interpreting it.
- Our approach
- Multi-agent orchestration across foundation models for reasoning, validation, and summarization layered directly onto existing analytics workflows.
- Outcome
- More than 30% reduction in manual data-task time for analyst teams, redirected toward interpretation and decision support.
AI Governance
- The problem
- Model risk, approval, and audit requirements are treated as compliance paperwork disconnected from how models are actually built and shipped.
- Our approach
- Governance built into the engineering lifecycle — versioned evaluation records, approval gates, and monitoring that produce an audit trail as a byproduct of normal development.
- Outcome
- Governance that satisfies regulators and internal risk teams without slowing model release cycles.
LLM Evaluation Framework
- The problem
- Teams ship LLM features with no consistent way to measure quality, regressions, or safety before or after release.
- Our approach
- LLM-as-jury evaluation harnesses with held-out benchmarks, calibrated judge models, and regression tracking wired into the deployment pipeline.
- Outcome
- A shared, quantitative quality bar that every model and prompt change is measured against before it reaches production.
RAG Platform
- The problem
- Most retrieval-augmented systems are prototyped quickly and fail quietly in production — wrong context, stale index, no way to trace an answer back to its source.
- Our approach
- Production-grade retrieval infrastructure with embedding pipeline management, index freshness controls, and citation-level traceability from answer to source.
- Outcome
- Retrieval systems that hold up under real usage volume and can be audited when an answer is questioned.
Decision Intelligence Platform
- The problem
- Data and models inform decisions, but the last mile — turning an insight into an action — still depends on a person noticing and acting.
- Our approach
- Systems that connect anomaly detection, forecasting, and reasoning directly to operational workflows and recommended actions.
- Outcome
- Decisions that happen closer to real time, with a smaller gap between detection and response.
Anomaly Detection Platform
- The problem
- Fragmented, surface-by-surface anomaly detection creates blind spots and inconsistent precision across a large transaction or event footprint.
- Our approach
- A unified detection framework with shared feature and evaluation standards, deployed independently across each surface but governed by one quality bar.
- Outcome
- Greater than 90% precision sustained across multiple transaction surfaces simultaneously, with a shared framework instead of six bespoke models.
Predictive Analytics
- The problem
- Forecasts built on historical averages break down exactly when the business needs them most — during volatility.
- Our approach
- Forecasting and risk models validated continuously against live outcomes, with retraining triggers tied to real drift, not a calendar.
- Outcome
- Forecasts that hold up under changing conditions and are trusted enough to drive planning decisions.
Customer Intelligence
- The problem
- Customer data lives in silos across product, support, and marketing systems, making a single coherent view of customer behavior nearly impossible.
- Our approach
- Unified customer data modeling with behavioral and lifecycle features engineered for both analytics and downstream ML consumption.
- Outcome
- A single, queryable view of customer behavior that powers segmentation, retention modeling, and personalization consistently.
Experimentation Platform
- The problem
- Teams run experiments with inconsistent statistical rigor, making it hard to trust results or compare initiatives against each other.
- Our approach
- Governed experimentation infrastructure with standardized statistical methodology, automated guardrail metrics, and centralized results tracking.
- Outcome
- Experiment results the organization can trust and compare consistently across teams and initiatives.
Ready to move your AI initiative from pilot to production?
Tell us about the problem you’re solving. We’ll tell you honestly whether it’s an AI problem, a data problem, or both.