Artificial Intelligence & Machine Learning

Take AI from prototype to production, reliably, securely, and at scale.

Most AI initiatives stall between the notebook and the real world. At Avighana, our MLOps and LLMOps services close that gap. We bring data science, engineering, and IT operations together on one automated, governed platform, so your machine learning and generative AI models are deployed faster, run reliably, and keep delivering value long after launch.

We design and automate every stage of the lifecycle:

• Data & feature pipelines: versioned, validated, and reproducible data preparation and feature management
• Experimentation & training: tracked experiments, automated training, and a central model registry
• CI/CD for ML: automated testing, packaging, and release of models and prompts across environments
• Scalable deployment: batch, real-time, and streaming inference on Azure, AWS, and Kubernetes
• Monitoring & observability: live tracking of performance, data drift, latency, and cost, with automated alerts and retraining triggers
• Governance & compliance: lineage, audit trails, access control, and responsible-AI guardrails built in from day one

Our LLMOps capabilities extend these practices to large language models and agentic AI systems:

• Prompt and model versioning, evaluation, and safe rollout
• Retrieval-augmented generation (RAG) pipelines with quality and grounding checks
• Guardrails for safety, hallucination control, and data privacy
• Monitoring and cost optimization for token usage and GPU infrastructure
• Orchestration and observability for multi-step AI agents

• Faster time to value: automated pipelines shorten the path from idea to production.
• Consistent performance: continuous monitoring catches drift and degradation before they affect your business.
• Security and compliance by design: your models and data stay protected and auditable.
• Scale without friction: infrastructure that grows with your data volumes and model complexity.
• Cloud-flexible: we work across Azure, AWS, and hybrid environments, using the tools your teams already know.
• Collaboration built in: shared workflows that keep data scientists, developers, and operations aligned.

Turn GenAI into measurable business value, securely, responsibly, and at enterprise scale.

Generative AI is changing how organizations create, decide, and serve their customers. But a demo is not a deployment. At Avighana, we design and build production-grade Generative AI solutions that are grounded in your data, integrated with your systems, and aligned with your brand, security, and compliance requirements.

• Enterprise AI assistants and copilots: conversational interfaces that answer questions, summarize documents, and support teams and customers using your own knowledge base
• Retrieval-augmented generation (RAG): accurate, source-grounded responses drawn from your internal documents, databases, and applications
• Content and creative automation: on-brand text, image, and document generation for marketing, sales, support, and operations
• Agentic AI systems: autonomous, multi-step agents that plan, use tools, and complete workflows with human oversight where it matters
• Intelligent document processing: extraction, classification, and summarization of contracts, invoices, reports, and other unstructured data
• Code and developer productivity tools: AI-assisted engineering, testing, and documentation workflows
• Synthetic data generation: realistic data for testing, analytics, and model training while protecting privacy

1. Discover: we identify high-value use cases and assess data readiness, risk, and expected ROI.
2. Design: we select the right models (commercial or open-source), architecture, and guardrails for your needs.
3. Build: we develop, fine-tune, and evaluate solutions with rigorous quality and safety testing.
4. Deploy: we integrate with your existing platforms on Azure, AWS, or hybrid environments, supported by MLOps and LLMOps practices.
5. Improve: we monitor quality, cost, and usage continuously and refine the solution over time.

Trust is the foundation of enterprise adoption. Every solution we deliver includes:

• Guardrails against harmful, off-brand, or inaccurate output
• Data privacy and access controls that keep sensitive information protected
• Human-in-the-loop review for high-stakes decisions
• Transparency, auditability, and alignment with your industry standards and regulatory obligations.

Build AI your customers, regulators, and employees can trust.

As AI moves into core business decisions, trust becomes a competitive advantage. Organizations need to show that their AI is fair, explainable, secure, and compliant, not just accurate. At Avighana, we help you embed ethics and governance into every stage of the AI lifecycle, so you can innovate with confidence and meet the expectations of customers and regulators.

We treat responsible AI as an engineering and operating discipline, not a policy document that sits on a shelf. We work with your business, legal, risk, and technology stakeholders to turn principles into practical controls that fit how your teams actually build and run AI.

• AI governance frameworks: clear policies, roles, and accountability structures, including AI councils, review boards, and approval workflows
• Risk and impact assessments: structured evaluation of AI use cases by risk level, intended use, and potential harm before they reach production
• Bias and fairness testing: rigorous evaluation of data and models across user groups, with documented mitigation where gaps are found
• Explainability and transparency: model documentation, decision explanations, and user-facing disclosures that make AI-driven outcomes understandable
• Privacy and data protection: consent, minimization, and access controls that protect sensitive and personal data
• Generative AI safeguards: guardrails against harmful output, hallucination, data leakage, and prompt-based attacks
• Continuous monitoring and audit: ongoing tracking of model behavior, drift, and policy compliance, with audit trails and evidence ready for review
• Training and change management: practical guidance for leaders, developers, and business users on responsible AI use

We design governance programs that map to recognized frameworks and evolving regulation, including:

• NIST AI Risk Management Framework
• ISO/IEC 42001 (AI management systems)
• EU AI Act requirements
• India’s Digital Personal Data Protection (DPDP) Act and global privacy laws such as GDPR
• Industry-specific requirements in sectors like financial services and healthcare.

Turn spoken words into accurate text, insights, and action in the languages your customers actually speak.

Voice is the most natural interface there is. Modern speech AI can now transcribe conversations, understand intent, and trigger actions in real time, even in noisy, multilingual, real-world conditions. At Avighana, we design and deploy speech recognition solutions that make your products more accessible, your operations more efficient, and your customer experiences more personal.

• Automatic speech recognition (ASR): accurate speech-to-text for live audio and recordings, including domain-specific vocabulary such as product names, medical terms, and financial language
• Voice assistants and voice bots: conversational agents for customer support, IVR modernization, and internal help desks, powered by speech recognition and GenAI
• Call and meeting transcription: searchable, speaker-labeled transcripts with summaries, action items, and sentiment analysis
• Contact center intelligence: real-time agent assist, quality monitoring, and compliance checks across every conversation
• Voice-enabled applications and devices: hands-free control for mobile apps, kiosks, vehicles, and industrial or field environments
• Multilingual and code-switching support: recognition across languages, accents, and dialects, including mixed-language speech such as Hindi-English
• Real-time translation and captioning: live subtitles and cross-language communication for meetings, events, and support
• Voice-driven data entry and documentation: dictation workflows that cut manual typing and reporting time
• Text-to-speech and voice synthesis: natural-sounding responses to complete the two-way voice experience

Speech AI has to work outside the lab. We engineer for:

• Noisy and variable audio: noise suppression, echo handling, and tuning for phone, headset, and far-field microphones
• Accents and dialects: model selection, fine-tuning, and custom vocabularies that reflect how your users really speak
• Low latency: streaming recognition for live conversations and instant responses
• Accuracy you can measure: benchmarking with word error rate and task-level metrics on your own data
• Continuous improvement: feedback loops and retraining pipelines (using MLOps practices) that keep models accurate as language and usage evolve

Voice data is personal data. Our solutions include:

• Encryption in transit and at rest, with strict access controls
• Options for cloud, private-cloud, or on-premises deployment
• Consent management, redaction of sensitive details such as card numbers and personal identifiers, and configurable data retention
• Alignment with applicable privacy regulation and your internal governance policies, supported by our AI Ethics and Governance practice

1. Discover: we define use cases, languages, audio conditions, and success metrics.
2. Design: we select and configure the right engine, whether commercial cloud APIs or open-source and custom models, for your accuracy, cost, and privacy needs.
3. Build and tune: we develop integrations, customize vocabularies, and fine-tune with your data.
4. Deploy: We integrate with your CRM, contact center, applications, and cloud platform on Azure, AWS, or hybrid environments.
5. Monitor and improve: we track accuracy, latency, and usage, and refine continuously.

• Business-first: we start from the outcome, whether that is shorter calls, faster documentation, or better accessibility.
• Technology-flexible: we choose the best engine for your use case rather than forcing one vendor.
• End-to-end capability: speech, language understanding, GenAI, cloud, and data engineering under one roof.
• Production-ready: scalable, monitored, secure, and built to run reliably.

Give your business the power to see, understand, and act on visual data.

Cameras, drones, scanners, and smartphones generate enormous volumes of images and video, yet most of it is never analyzed. Computer vision turns that visual data into real-time insight and automated action. At Avighana, we design and deploy production-grade computer vision solutions that improve quality, safety, efficiency, and customer experience across industries.

• Object detection and tracking: locating and following people, vehicles, products, and equipment in images and live video
• Image classification and segmentation: categorizing and precisely outlining items, regions, and anomalies
• Visual quality inspection: automated defect detection on production lines, with consistent accuracy at speed
• Optical character recognition (OCR) and document vision: extracting text and data from invoices, IDs, forms, and handwritten records
• Video analytics: counting, behavior and flow analysis, and event detection for retail, facilities, and public spaces
• Safety and compliance monitoring: detecting PPE use, restricted-zone entry, and unsafe conditions in real time
• Medical and scientific imaging support: tools that help trained professionals review and prioritize scans and images
• Visual search and recommendation: finding similar products and content from a photo
• Multimodal AI: combining vision with language models so systems can describe, search, and answer questions about images and video
• Edge and embedded vision: low-latency inference on cameras and devices where cloud connectivity is limited

• Manufacturing: defect detection, assembly verification, predictive maintenance, and worker safety
• Retail and e-commerce: shelf and inventory monitoring, footfall analytics, checkout automation, and visual product search
• Healthcare and life sciences: imaging workflow support, with clinicians always making the final decision
• Security and smart infrastructure: perimeter monitoring, access control, traffic and crowd analytics
• Logistics and transportation: package and vehicle recognition, damage detection, and yard automation
• Agriculture and energy: crop and asset monitoring through drone and satellite imagery
• Banking and insurance: document capture, identity verification workflows, and claims assessment from photos

Vision systems must work outside the lab, in varied lighting, angles, weather, and image quality. We focus on:

• Data strategy: collection, labeling, and augmentation, including synthetic data where real examples are scarce
• Model selection and tuning: modern deep learning architectures, including transformer-based and foundation models, fine-tuned on your data
• Measurable accuracy: benchmarking with precision, recall, and task-level metrics on your own images
• Performance and cost: optimization for real-time speed on cloud GPUs or edge devices
• Continuous improvement: monitoring for drift and retraining pipelines through MLOps practices, so accuracy holds as conditions change

Visual data can be sensitive. For use cases involving people, including face-related capabilities, we apply:

• Privacy-by-design practices such as on-device processing, blurring or anonymization, and minimal data retention
• Consent, transparency, and access controls
• Bias and fairness testing across demographics and conditions
• Human review for high-stakes decisions
• Alignment with applicable laws and your governance policies, supported by our AI Ethics and Governance practice