A turnkey offering that provides businesses on-demand access to AI capabilities — model hosting, APIs, compute provisioning, monitoring and lifecycle management — without heavy upfront infrastructure investment. The service bundles prebuilt models, custom model deployment, integration endpoints, and managed operational tasks (scaling, backups, monitoring). Customers get pay-as-you-go pricing, SLAs for uptime, and optional developer support. It lowers the entry barrier and accelerates time-to-value for AI projects.
Performance Metrics: Time-to-production reduction from 6–12 weeks to 1–3 weeks, TCO reduction of 20–50%, SLA target 99.5%–99.9%, API latency 50–300 ms per request.
Use Cases: Customer service automation, document Q&A, recommendation APIs for e-commerce, fraud scoring for finance, image recognition for retail/inspection, personalized marketing.

Technical service to embed AI models and pipelines into existing applications, databases, and business processes. Covers API design, data connectors, authentication, workflow integration, and UI/UX hooks so model outputs can be consumed by business applications. Also includes data-pipeline alignment, latency/throughput tuning and fallback strategies for model failures.
Performance Metrics: Integration lead time 2–8 weeks, error rate <1–3%, additional latency <20–200 ms, 10–30% improvement in first-call resolution.
Use Cases: ERP/CRM augmentation, customer-service platforms, supply-chain orchestration, legacy system modernization with AI inference.

Design, tune, and deploy generative models for text, image, audio and multimodal outputs: content generation, summarization, code synthesis, image generation, and document conversationalization. Includes fine-tuning on customer data, prompt engineering, content filtering and safety layers, embedding stores for retrieval augmentation, and UI components for creative workflows.
Performance Metrics: User satisfaction 80–95%, response correctness 85–98%, throughput tens to thousands of calls per minute, cost reduction 20–60%.
Use Cases: Marketing copy generation, automated report drafting, legal/compliance document summarization, conversational document assistants, creative design assets.

End-to-end model development: problem framing, data collection & labeling, feature engineering, model selection and training, hyperparameter tuning, validation, and productionization. This also covers model retraining strategy, A/B testing, and transfer learning to accelerate results for domain-specific needs.
Performance Metrics: Model accuracy improvement +10–40%, time to first prototype 2–6 weeks, manual effort reduction 30–80%, inference throughput 10s to 1000s req/sec.
Use Cases: Fraud detection, customer churn prediction, image classification, demand forecasting, predictive maintenance.

Advisory services that help organizations form AI strategy, evaluate use cases, prioritize roadmap, build governance, and estimate ROI. Includes data maturity assessment, AI capability gap analysis, vendor selection, and implementation roadmaps. Often delivered as workshops, PoC planning, and executive briefings to accelerate enterprise adoption.
Performance Metrics: ROI estimation accuracy ±10–30%, time to prioritized roadmap 2–6 weeks, 30–70% of recommended use cases operationalized within 12 months.
Use Cases: C-suite digital transformation programs, compliance planning, and cross-business unit AI adoption.

Design and deploy conversational agents for web, mobile, and voice interfaces. Services include dialog design, intent/entity extraction, multi-turn context handling, handoff logic to humans, omnichannel integration (messaging, web, voice), and analytics for conversational metrics. Also includes customization for domain knowledge and persona tuning.
Performance Metrics: Automation rate 40–85%, average handle time reduction 20–60%, CSAT improvement +5–20 points, intent recognition accuracy 85–98%.
Use Cases: Customer support, IT helpdesk, HR self-service, lead qualification, voice assistants.

Personalization systems that combine collaborative filtering, content-based methods and hybrid models to suggest products, content or actions. Services include data ingestion, candidate retrieval, ranking models, online/offline training pipelines, A/B testing frameworks and feedback loops to continuously improve recommendations.
Performance Metrics: CTR uplift 10–50%, conversion uplift 5–30%, recommendation latency <50–200 ms, churn reduction 5–25%.
Use Cases: E-commerce product suggestions, media content recommendation, in-app personalization, marketing offers.

Development and deployment of models to detect, classify, segment, and describe images and videos. Includes object detection, OCR, facial analytics (where compliant), anomaly detection on visual production lines, and video analytics for safety and operations. Also provides annotation pipelines and edge deployment for low-latency inference.
Performance Metrics: Classification accuracy 85–99%, FNR <5–15%, edge inference latency 10–200 ms, manual inspection time reduction 50–95%.
Use Cases: Manufacturing visual inspection, retail shelf analytics, medical imaging support, security & surveillance, autonomous inspection drones.

Services to extract meaning from text and speech: classification, named-entity recognition, intent detection, summarization, translation, and automated speech recognition (ASR). Also offers domain adaptation, fine-tuning, speaker diarization, and pipelines that combine ASR + NLP for end-to-end voice-driven apps.
Performance Metrics: ASR WER 5–20%, intent classification accuracy 85–98%, summarization usefulness 80–95%, speech-to-text + intent latency 200–800 ms.
Use Cases: Contact center transcription, voice assistants, call center analytics, compliance monitoring, content indexing.

Time-series forecasting, demand prediction, and predictive scoring services that turn historical data into actionable forecasts. Includes seasonality detection, causal modeling, scenario simulation, and probabilistic forecasts. Delivered as APIs, dashboards and scheduled reports.
Performance Metrics: Forecast accuracy MAPE 5–30%, inventory stockout reduction 20–60%, forecast lead time extension 1–4 weeks.
Use Cases: Retail demand planning, revenue forecasting, energy load forecasting, predictive maintenance scheduling.

Foundational services to design and build data lakes/warehouses, ETL/ELT pipelines, feature stores, and data quality frameworks needed for AI/ML workflows. Includes schema design, metadata management, streaming ingestion, and batch processing to deliver reliable, production-ready datasets.
Performance Metrics: Data ingestion throughput MB/s to GB/s, data freshness seconds to hours, bad data incidents reduction 50–95%, ML engineer time saved 30–70%.
Use Cases: Any AI/ML initiative requiring structured, reliable inputs: finance, retail, healthcare, IoT.

Build customer-owned AI products: from PoC to MVP to fully scaled SaaS or on-prem systems. Services include product design, UX, multi-tenant architecture, paywall and licensing support, analytics and telemetry, and commercialization planning. Focus is on creating repeatable, productized AI capabilities.
Performance Metrics: Time to MVP 6–16 weeks, product adoption 10–30% weekly active user uptake, time to breakeven 6–12 months.
Use Cases: Vertical AI SaaS (healthcare triage tools, legal research platforms), internal AI tooling, analytics products.

Combine robotic process automation with cognitive AI (document understanding, OCR, classification) to automate structured and unstructured business processes. Services include building bots, integrating with ERPs, orchestrating workflows, and adding ML models for decisioning. Emphasis on low-code automation for high ROI.
Performance Metrics: Process automation rate 30–95%, cost reduction per transaction 20–80%, processing throughput increase 10x–100x, error rate <1–3%.
Use Cases: Invoice processing, claims handling, KYC/AML document processing, HR onboarding.

Provide on-demand AI talent (data scientists, ML engineers, MLOps engineers, labelers) embedded with client teams. Services include team set-up, ramping, knowledge transfer, and flexible engagement models (fixed-term, long-term, or project-based). The approach accelerates hiring while preserving governance and IP rules.
Performance Metrics: Ramp-up time 2–6 weeks, cost vs local hiring 15–60% lower, retention >80%, productivity uplift +20–50%.
Use Cases: Enterprises needing rapid capacity: fintech, product firms, research teams, and R&D centers.

End-to-end operationalization of ML: automated model training, validation, deployment, versioning, drift detection, retraining pipelines, and governance. Includes model registry, feature store integration, canary/blue-green rollouts, and monitoring dashboards to manage models in production reliably.
Performance Metrics: Mean time to deploy hours to 2 days, model drift detection real-time to hours, model rollback minutes, incident reduction 40–90%.
Use Cases: Any production ML scenario: risk scoring, real-time recommendations, fraud detection.

Optimize and deploy models to edge devices (mobile, embedded, gateway) to enable low-latency, offline, and privacy-preserving inference. Services include model quantization, pruning, compilation for hardware accelerators, and secure update mechanisms. Focuses on balancing accuracy vs resource constraints.
Performance Metrics: Inference latency <10 ms to 200 ms, model size reduction 2×–20×, energy consumption reduction 30–90%, offline operation 100% availability.
Use Cases: Industrial IoT inspection, mobile on-device personalization, automotive ADAS features, retail kiosks, smart cameras.

Establish policies, processes, and tooling for ethical and compliant AI: bias detection & mitigation, explainability, audit trails, data lineage, consent management, and standards compliance. Includes impact assessments, model documentation and human-in-the-loop controls to align AI with regulatory and ethical frameworks.
Performance Metrics: Bias reduction 20–80%, explainability coverage >90%, time to audit readiness 2–6 weeks, compliance posture improvement measurable.
Use Cases: Finance (credit, lending), healthcare diagnostics, HR (hiring systems), public sector decisioning.

Generate labeled synthetic datasets and augment real data to improve model training where data is scarce or sensitive. Techniques include simulation, generative models (for tabular, image, text), and programmatic augmentation. Also provides privacy-preserving synthetic data for sharing and testing.
Performance Metrics: Model performance lift +5–40%, reduced labeling need 30–90%, privacy risk reduction measurable, data diversity increase 10% to 30% minority class.
Use Cases: Autonomous driving simulation, medical imaging where real data is limited, fraud scenario generation, corner-case generation for safety testing.

Provide AutoML solutions that automatically explore model architectures, hyperparameters and feature engineering to accelerate prototype building. Services include custom AutoML pipelines tuned for client data, model explainability outputs and handoff to MLOps for deployment. Useful to non-ML teams to quickly validate ideas.
Performance Metrics: Time to prototype hours to days, model performance gap 0–10%, resource consumption reduction 20–60%, coverage dozens to hundreds of models.
Use Cases: Business forecasting, churn prediction, marketing response modeling, rapid PoC generation.

Assess and harden AI systems against adversarial inputs, data poisoning, model extraction and other attacks. Services include adversarial testing, robustness evaluation, threat modeling, secure model packaging, and penetration tests for model APIs. Also provides runtime protection strategies, anomaly detection for suspicious usage patterns and rate limits.
Performance Metrics: Robustness improvement 50–99%, attack detection latency seconds to minutes, model extraction risk mitigation 10×–1000× queries needed, false positive reduction 10–50%.
Use Cases: Any exposed ML API, financial risk models, authentication & biometric systems, safety-critical systems (automotive/medical).