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Digital & Intelligence Engineering · Enterprise intelligence

Generative and Predictive AI

Production-grade AI that creates, predicts, and helps teams act with confidence.

Generative and predictive AI network turning enterprise data into forecasts and decision signals
Grounded generative AI & predictive intelligence

The work

Engineering that holds up beyond the demo.

We turn enterprise data into grounded generative experiences and actionable predictions. Our teams combine foundation models, retrieval-augmented generation, vector search, deep learning, forecasting, and explainability so AI can work with proprietary context, support real decisions, and move from a promising pilot into a measurable production capability.

What we help you achieve

  • Grounded answers and recommendations tied to trusted enterprise knowledge
  • Faster time from operational data to useful insight
  • Forecasts and anomaly signals connected to decisions and workflows
  • Explainable, measurable model behavior with visible uncertainty
  • A practical path from experiment to secure production deployment

Detailed capabilities

The work behind a dependable platform.

Explore the engineering areas that take this capability from a first technical decision to a stable, production-ready system.

01

Enterprise AI Strategy & Use-Case Architecture

AI initiatives shaped around business decisions, data readiness, risk, and the operating workflow that must change.

  • Use-case discovery across knowledge work, customer operations, engineering, service, supply chain, finance, and product experiences
  • Architecture choices across hosted and open foundation models, small language models, predictive engines, classical ML, rules, and human review
  • Value, feasibility, risk, latency, privacy, cost, and adoption criteria that turn an AI idea into an executable roadmap
  • System boundaries for data sources, retrieval, inference, tools, applications, analytics, identity, audit, and feedback
  • Pilot-to-production gates with measurable success criteria, ownership, rollout phases, and a plan for continuous improvement
02

Knowledge Grounding, RAG & Vector Search

Enterprise knowledge made discoverable and usable without asking a model to invent what the business already knows.

  • Source connectors, document parsing, metadata extraction, classification, chunking, normalization, and access-aware ingestion
  • Embedding generation, vector and hybrid search, semantic reranking, filters, freshness, tenant isolation, and retrieval evaluation
  • Retrieval-augmented generation for policies, technical documentation, service knowledge, product information, and operational records
  • Citation, provenance, source linking, context budgeting, conflict handling, stale-content detection, and answer abstention
  • Ingestion and re-indexing workflows that keep private, changing enterprise knowledge aligned with production behavior
03

Generative & Multimodal Systems

Generative experiences engineered around the content, tools, modalities, and controls required by the product.

  • Text, code, document, image, audio, video, and multimodal workflows selected around user intent and measurable outcomes
  • Prompt and template design, structured outputs, tool calling, function schemas, context management, and conversation state
  • Agent or workflow integration with enterprise APIs, search, databases, ticketing, document systems, and approval checkpoints
  • Model routing, fallback, caching, batching, streaming, token budgeting, and latency controls for reliable user experiences
  • Content quality, factuality, tone, safety, privacy, copyright, escalation, and human-in-the-loop review patterns
04

Predictive Intelligence & Decision Support

Forecasting and signal detection built to explain what is likely to happen and help teams decide what to do next.

  • Time-series forecasting, classification, regression, ranking, recommendation, anomaly detection, and risk scoring
  • Feature engineering, data quality checks, temporal validation, leakage prevention, imbalance handling, and cohort analysis
  • Ensemble learning, gradient boosting, deep neural networks, probabilistic outputs, calibration, and threshold selection
  • Scenario analysis, what-if exploration, leading indicators, confidence intervals, causal considerations, and intervention tracking
  • Operational delivery through dashboards, alerts, APIs, workflow actions, and human approval rather than isolated model scores
05

Evaluation, Explainability & Responsible AI

Model behavior assessed against real tasks, failure costs, and trust requirements before it is allowed to influence decisions.

  • Golden datasets, task-specific test sets, reference answers, annotation guidance, and representative edge-case libraries
  • Generative evaluation for groundedness, relevance, completeness, citation quality, refusal behavior, toxicity, and hallucination risk
  • Predictive evaluation for precision, recall, F1, calibration, lift, ranking quality, forecast error, drift, and business impact
  • Explainability through feature attribution, local and global drivers, counterfactuals, confidence, uncertainty, and decision traceability
  • Red-team scenarios, privacy checks, bias analysis, abuse testing, human review, model cards, limitations, and approval gates
06

Production Integration & Deployment

AI connected to the product and operating environment with the reliability, security, and feedback loops required to sustain value.

  • Inference APIs, batch pipelines, event-driven workflows, streaming responses, SDKs, dashboards, and embedded product experiences
  • Identity-aware access, tenant isolation, encryption, secrets handling, PII controls, audit logs, and policy enforcement
  • Cloud, edge, and hybrid deployment decisions based on latency, data residency, cost, connectivity, and hardware constraints
  • Observability for latency, throughput, token use, retrieval quality, confidence, user feedback, errors, and fallback behavior
  • Versioned models, prompts, retrieval indexes, datasets, features, and application contracts with rollback and iteration plans

Technical scope

Engineering detail from reset to runtime.

The work is designed around observable system behavior: what executes first, how resources are controlled, how failures are recovered, and how the platform proves it is ready.

01

AI architecture & data foundations

  • Use-case value, feasibility, risk, model selection, hosted/open models, small language models, predictive engines, rules, human review, identity, audit, and rollout gates
  • Structured and unstructured source ingestion, parsing, metadata, access controls, normalization, data quality, lineage, privacy, and freshness
  • Feature engineering, temporal splits, leakage prevention, embeddings, chunking, indexing, tenant isolation, and versioned data contracts
02

Generative systems & RAG

  • Prompt and template management, structured outputs, tool calling, context windows, conversation state, model routing, fallback, caching, streaming, and token budgets
  • Vector and hybrid search, reranking, filters, citations, provenance, context assembly, stale-content detection, conflict handling, and abstention
  • Text, code, document, image, audio, video, and multimodal workflows integrated with APIs, knowledge systems, approvals, and user-facing products
03

Predictive & explainable intelligence

  • Forecasting, classification, regression, ranking, recommendation, anomaly detection, risk scoring, ensembles, gradient boosting, deep neural networks, and probabilistic outputs
  • Precision, recall, F1, calibration, lift, ranking quality, forecast error, confidence intervals, drift, cohort analysis, and business-impact measurement
  • Feature attribution, counterfactuals, uncertainty, drivers, scenarios, what-if analysis, decision traces, alerts, dashboards, APIs, and workflow actions
04

Evaluation & production integration

  • Golden datasets, reference answers, annotations, groundedness, relevance, completeness, citation quality, safety, privacy, bias, abuse, and red-team evaluation
  • Inference APIs, batch and event pipelines, streaming, cloud/edge/hybrid deployment, data residency, latency, throughput, cost, and connectivity constraints
  • Model, prompt, dataset, feature, retrieval-index, application-contract, and configuration versioning with monitoring, feedback, rollback, and approval

Services and customer value

Technical work with a business reason.

Every engineering activity is tied to a decision your team needs to make: bring up faster, reduce risk, improve determinism, or prove production readiness.

01

Enterprise AI strategy and architecture

Technical work

Map high-value use cases, data sources, model choices, workflow boundaries, human controls, risk, cost, and pilot-to-production gates into an executable architecture.

Customer benefit

Teams invest in AI capabilities that have a clear operating owner, measurable value, and a credible route beyond a disconnected proof of concept.

02

Grounded knowledge assistants and RAG

Technical work

Build access-aware ingestion, parsing, chunking, embeddings, vector or hybrid search, reranking, context assembly, citations, provenance, freshness, and abstention.

Customer benefit

People can find and use proprietary knowledge faster while reducing unsupported answers and preserving trust in the sources behind each response.

03

Generative and multimodal workflows

Technical work

Design prompt, tool-use, structured-output, model-routing, caching, streaming, multimodal, and approval patterns for content, code, document, and decision workflows.

Customer benefit

AI becomes a useful part of the product or operation, with predictable interaction patterns instead of an isolated chat surface.

04

Predictive intelligence and forecasting

Technical work

Develop forecasting, classification, ranking, recommendation, anomaly, and risk models with feature engineering, temporal validation, calibration, scenarios, and action delivery.

Customer benefit

Teams see earlier signals and can connect forecasts to specific interventions, improving decision speed without hiding uncertainty.

05

Explainability and responsible AI

Technical work

Create evaluation sets, reference outputs, quality metrics, feature drivers, counterfactuals, confidence measures, red-team tests, privacy checks, and human approval gates.

Customer benefit

Stakeholders can understand limitations, investigate failures, and govern AI behavior before it affects customers, employees, or regulated decisions.

06

Production AI integration

Technical work

Connect models to APIs, products, dashboards, event pipelines, identity, observability, feedback, versioning, rollback, and cloud, edge, or hybrid runtime environments.

Customer benefit

The organization gets a maintainable production capability with visible operating costs, measurable behavior, and a feedback loop for improvement.

Why this matters for AI

Intelligence needs a dependable execution layer.

Generative and predictive systems are strongest when they operate as one decision layer: retrieval grounds what the system knows, generative models create useful responses or actions, predictive models anticipate change, and explainability makes the result reviewable. We connect those capabilities to real data, workflows, product interfaces, and operational controls so AI can create measurable value without becoming an opaque experiment.

What you take forward

Clear outputs your team can build on.

  • AI use-case map, value hypothesis, risk register, data-readiness assessment, model-selection rationale, target architecture, ownership model, and pilot-to-production roadmap
  • Grounded knowledge pipeline with source connectors, parsing, metadata, access controls, chunking, embeddings, vector/hybrid search, reranking, citations, provenance, and freshness controls
  • Generative workflow package with prompts, templates, tool contracts, structured outputs, model routing, caching, streaming, safety controls, fallback, and review checkpoints
  • Predictive modeling package with feature definitions, training/evaluation splits, baselines, forecasts, anomaly or risk signals, calibration, scenarios, and decision-delivery interfaces
  • Evaluation and responsible-AI pack with golden datasets, reference outputs, quality metrics, explainability, uncertainty, red-team cases, privacy/bias checks, limitations, and approval gates
  • Production integration handoff with APIs, deployment configuration, identity and audit controls, observability dashboards, model/prompt/data/index versioning, feedback, rollback, and runbooks

Capability map

Built across the layers.

The right answer is usually a connected system of decisions, not a single isolated skill.

01

LLM, multimodal, and retrieval-augmented generation architecture

02

Enterprise data ingestion, chunking, embeddings, and vector search

03

Predictive forecasting, classification, anomaly detection, and ranking

04

Explainable AI, confidence scoring, and decision-support experiences

05

Evaluation datasets, quality metrics, guardrails, and human review

06

Model serving, application integration, monitoring, and iteration loops

Keep exploring

See how the pieces connect.