AI, ML and Data Engineering Services That Ship to Production

LLM apps that earn their keep

Copilots, search, summarization and document workflows tied to measurable KPIs. We ship features that move metrics, not demos that stall in pilot.

RAG that actually retrieves

Hybrid retrieval, reranking, evaluation and observability. We tune on your data, your queries and your acceptance criteria, not on toy benchmarks.

MLOps that scales

Reproducible training, model registries, shadow deploys and monitoring for drift and bias. Every model has a clear path from notebook to production.

Data platforms that hold up

Modern data stack on Snowflake, BigQuery or Databricks. ELT with dbt, contracts between teams, and lineage your auditors and analysts can both trust.

EU AI Act + NIST AI RMF conscious

Risk classification, transparency notices, technical documentation and human oversight built into the product under both the EU AI Act and the NIST AI Risk Management Framework — not bolted on before an audit. State-law aware (Colorado AI Act 2026, NYC AEDT, CCPA ADM rules).

Evals as a habit

Golden datasets, automated regressions and offline evals on every prompt and model change. Quality is a number, not a feeling.

Fixed Price

For bounded AI proofs of value, RAG pilots and data platform builds with crisp acceptance criteria and a fixed deadline.

Time & Materials

For evolving AI products where prompts, models and metrics change weekly. Senior squad, weekly demos, monthly capacity reviews.

Dedicated Team

A long-running AI and data squad embedded in your product organization, owning data quality, model lifecycle and compliance documentation.

FinTech

Real-time fraud scoring requires ML models that evaluate 200+ features per transaction in under 50ms. We train gradient boosting models (LightGBM, XGBoost) and neural networks on transaction histories, device fingerprints, and behavioral biometrics, deploying them through feature stores (Feast) and low-latency inference servers (TorchServe, Triton) that meet financial institution SLA requirements.

Credit risk models for lending decisioning, CLV prediction for customer segmentation, and AML transaction monitoring models using graph neural networks to detect money laundering networks are all production AI applications we build and maintain for FinTech clients. We design model governance frameworks with model cards, bias evaluation on protected attributes, and regulatory explainability documentation for OCC and FCA model risk management requirements. See FinTech.

HealthTech

Medical imaging AI for radiology triage, pathology slide analysis, and ophthalmology screening requires DICOM-compliant data pipelines, FDA SaMD regulatory planning, and clinical validation studies before deployment. We build PyTorch imaging analysis pipelines using MONAI framework, implement de-identification workflows compliant with HIPAA Safe Harbor and Expert Determination methods, and design IRB-approved clinical study protocols for AI performance validation.

Clinical NLP systems extract structured data from unstructured clinical notes for ICD-10 coding, population health analytics, and clinical trial recruitment using domain-adapted models fine-tuned on clinical text (BioBERT, ClinicalBERT). We implement HIPAA-compliant NLP pipelines with de-identification pre-processing and audit logging, and design annotation workflows with clinical expert review for ground truth label quality. See HealthTech.

E-commerce & Retail

Recommendation systems driving 35–40% of e-commerce revenue require real-time inference on session behavior signals, not just historical purchase data. We build two-stage retrieval-and-ranking architectures: a fast Approximate Nearest Neighbor (ANN) retrieval layer using FAISS or ScaNN that retrieves 100 candidates in under 5ms, followed by a ranking model that scores candidates on business-aware features (margin, inventory, recency) before serving the final ranked list.

Dynamic pricing models optimize markdown timing for perishable inventory, promotional discount allocation across customer segments, and real-time competitive price matching using scraping pipelines and pricing elasticity models. We implement A/B testing infrastructure for pricing experiments with proper holdout groups and statistical significance testing, measuring incremental revenue impact rather than average selling price. See E-commerce.

Manufacturing & Industrial

Predictive maintenance models reduce unplanned downtime by 30–50% by detecting equipment failure signatures in sensor time-series data before they manifest as breakdowns. We build LSTM and Transformer-based anomaly detection models for vibration, temperature, and pressure sensor streams, deploy inference on edge devices (NVIDIA Jetson, industrial PCs) for sub-second alerting without cloud round-trip latency, and integrate with CMMS (Computerized Maintenance Management Systems) to auto-generate work orders when anomaly scores exceed thresholds.

Computer vision quality inspection systems replace manual visual inspection on production lines with camera arrays processing 100+ frames per second through defect detection models (YOLO, Detectron2) that catch surface defects, dimensional deviations, and assembly errors that human inspectors miss under fatigue conditions. We design training data collection workflows, annotation tooling, and active learning pipelines that continuously improve model accuracy as new defect types emerge.

Logistics & Supply Chain

Demand forecasting models for supply chain planning use hierarchical time-series techniques (Temporal Fusion Transformer, Prophet, ARIMA with exogenous variables) that account for promotional calendars, seasonality, and external demand signals (weather, economic indicators). We build forecasting pipelines that generate item-location-week demand forecasts at scale (millions of SKUs), integrated with ERP systems for automatic purchase order generation when inventory levels breach reorder points. See Logistics.

Route optimization using ML-enhanced graph algorithms reduces last-mile delivery cost per package by identifying optimal sequencing of delivery stops under real-world constraints (time windows, vehicle capacity, driver break requirements). We build route optimization APIs using OR-Tools and commercial solvers (Gurobi), augmented with ML prediction of address-level delivery time distributions that improve estimated time of arrival accuracy for customer-facing tracking interfaces.

SaaS & Enterprise AI

Generative AI integration in SaaS products (AI-assisted writing, code completion, document Q&A, intelligent search) requires robust LLM orchestration, prompt versioning, and output validation infrastructure that goes beyond direct API calls. We build LLM orchestration layers using LangChain and LlamaIndex with structured output parsing, implement prompt management systems with A/B testing capabilities for continuous optimization, and design RAG pipelines with vector databases (Pinecone, pgvector) that ground LLM responses in company knowledge bases.

Enterprise AI product features must address data privacy (customer data must not be used for model training by third-party LLM providers), latency (generative AI response time must not degrade perceived product speed), and cost (LLM API costs at scale can exceed infrastructure costs). We design cost-optimized LLM architectures with intelligent routing between GPT-4-level models for complex tasks and smaller models (Llama 3, Mistral) for routine tasks, with semantic caching to avoid redundant expensive API calls.

When to fine-tune vs. use GPT-4 API

Using GPT-4 API is the right choice for general-purpose tasks where domain-specific accuracy is not critical, time-to-market is paramount, and per-query costs are acceptable at your expected volume. Fine-tuning open-source models (Llama 3 70B, Mistral 7B) becomes economically justified when you process more than 1 million tokens per day (API cost exceeds GPU hosting cost), need sub-500ms inference latency, require data to remain on-premise for compliance reasons, or need consistent output format that prompt engineering alone cannot reliably achieve.

LoRA and QLoRA parameter-efficient fine-tuning techniques reduce the GPU memory and training time required for fine-tuning large models by 80–90%, making fine-tuning accessible on single A100 GPUs or cloud spot instances. We design fine-tuning pipelines, curate domain-specific training datasets, and run systematic benchmark evaluations comparing the fine-tuned model against GPT-4 baseline on your specific task before committing to the infrastructure investment.

Open-source LLMs vs. proprietary models

Open-source LLMs (Llama 3, Mistral, Falcon, Qwen) give you complete control over data handling, enable offline and air-gapped deployments, eliminate per-token API costs at scale, and can be customized without vendor permission. Proprietary models (GPT-4, Claude, Gemini Ultra) offer state-of-the-art performance on complex reasoning tasks, require no infrastructure management, and provide faster time-to-pilot.

The decision depends on your data sensitivity requirements, inference volume, performance benchmarks on your specific tasks, and organizational AI capability. For healthcare and financial services with strict data residency requirements, open-source models deployed on private infrastructure are often mandatory. For product teams building AI-assisted features quickly, proprietary APIs with strong rate limits and consistent performance are usually the pragmatic starting point.

On-prem AI inference vs. cloud GPUs

Cloud GPU inference (AWS SageMaker, GCP Vertex AI, Azure ML) is optimal for variable workloads, pilot projects, and teams without GPU cluster management expertise, with costs ranging from $2–8/hour per A100 GPU. On-premises inference becomes cost-effective when GPU utilization exceeds 60% consistently, when regulatory requirements prohibit sending data to cloud providers, or when inference latency targets cannot be met over cloud API round-trips.

We analyze your workload profile, data residency requirements, and cost projection over 3 years to recommend the right infrastructure strategy. For most enterprise AI applications, a hybrid approach — cloud GPUs for model training and batch inference, on-prem or dedicated cloud instances for real-time serving of production models — balances cost efficiency with latency requirements and regulatory compliance.