As an Artificial Intelligence development company, we house an expert team of AI engineers who work with the latest tech stacks to build AI solutions that can transform your enterprise. The result is not just automation, but a more intelligent, insight-driven operational structure for your business.
TechnoDX has over 15 years of experience providing world class AI strategy and engineering services to our global partners.
Years in AI, ML, and Product Engineering
Global Enterprise Clients
Deployed AI models
LLM & Agent Solutions Delivered in the Last 3 Years
Industry-Specific AI Accelerators Built In-House
Our motto is to design quality AI solutions that seamlessly merge with our clients’ workflows and business requirements. To maintain that quality, we build every solution on a modern AI backbone combining LLMs, vision & audio models, RAG pipelines, optimized inference engines, end-to-end MLOps pipelines, and more.
The result? Boosted operational efficiency with AI systems that improve decision accuracy, reduce analysis delays, and proactively flag operational risks.
Our AI consulting services will help you easily identify where AI can create real business value. We use real data and KPIs to create PoCs and perform use-case prioritization, feasibility checks, ROI estimates, and risk assessments to validate your AI initiative’s feasibility prior to full-scale development.
From strategy to deployment and beyond, we aid you along every step to implement AI that fits your existing workflows and operations like a glove
Clear validation with PoCs before development.
AI systems designed around your workflows and teams.
Built-in security and compliance based on your industry and location.
Long-term reliable performance and governance.
Ready to make your dream AI solution a reality?
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As an AI software development company, we are committed to building AI solutions that help businesses automate and modernize their entire infrastructure. Our core AI services, combined with industry-best engineering practices and the latest tech stacks, forms the foundation on which we build each solution.
Enterprise-grade generative AI solutions designed to convert language, content, and institutional knowledge into active AI tools.
Enterprise chatbots & AI assistants
RAG-based enterprise search
Text and content generation
LLM-powered workflows and agents
Multimodal AI solutions (text, vision, speech)
Model selection and instruction tuning
Fine-tuning with PEFT, LoRA, QLoRA
Retrieval-Augmented Generation (RAG)
Prompt engineering and orchestration
Guardrails, governance, and cost controls
LLM orchestration: LangChain, LlamaIndex
Foundation models: GPT-class, Claude-class, Hugging Face
Vector stores: FAISS, Chroma, Pinecone
Embeddings & document pipelines
Speech & multimodal tooling (Whisper-based stacks)
We select the correct GPT-class/Claude-class language models, as well as open-source foundation models, and tune them based on your accuracy, cost, latency, and deployment needs. If required, we will fine-tune and domain-adapt models to align with enterprise workflows and governance requirements.
Our NLP stack gives your systems the ability to understand, classify, extract, and generate language. This is done with the help of transformer-based models, semantic embeddings, multilingual pipelines, and domain-specific text processing that provides search, automation, and decision support.
We use the most modern orchestration frameworks to design retrieval-augmented generation pipelines and agent-based workflows. These frameworks manage context retrieval, tool use, memory, and execution control to ensure that your systems exhibit reliable and grounded AI behavior.
Our ML and deep learning stack supports features like forecasting, anomaly detection, classification, and optimization use cases. We use industry-best frameworks to train and deploy models that can balance accuracy, interpretability, and performance in your production environments.
Vector search and knowledge retrieval systems are used to power our semantic search and enterprise RAG solutions. These systems are designed to give you high-quality retrieval, relevance tuning, and knowledge access across all your enterprise document collections.
Our data & analytics stack is built around the Python ecosystem and modern data platforms to support both batch and streaming workloads. It allows us to provide reliable data ingestion, transformation, and analytics across all your structured and unstructured data sources.
Every AI system we build is deployed using CI/CD pipelines and containerized runtimes across cloud, on-premise, and edge environments. We use observability tooling that provides visibility into performance, drift, latency, usage, and cost, so your AI systems can remain stable and accountable in production.
We integrate security and governance tooling into your AI systems to protect it from misuse and data leakage while maintaining auditability. This includes implementing access controls, prompt and input protection, usage monitoring, explainability, and compliance-aligned controls across the AI lifecycle.
Our AI models connect data across your systems to identify patterns and risks that might otherwise go unnoticed.
Our AI systems analyze large volumes of data and recommend next-best actions to help your teams make faster, better decisions without manual intervention.
We embed predictions, alerts, and recommendations into workflows to help your teams act on insights without delay.
Our custom AI models can integrate smoothly with your current ERP, CRM, OT, and cloud platforms, without the need for replacements.
We set up strong MLOps, monitoring, and governance frameworks to keep your AI systems performing stable and secure over time.
Ethical AI safeguards are embedded into all our engineering workflows to ensure fairness, explainability, compliance, and human oversight.
30–50% increase in throughput without adding any new staffUp to 325% ROI within the first 12–18 months80% faster automated document analysis and interpretation15,000+ hours saved per month through faster document-driven decision-makingAI initiatives launched 30–60% faster using phased pilotsPoC-to-production cycles reduced from months to 8–12 weeks15–40% boost in prediction accuracy after domain tuningError rates reduced by 20–50% compared to rule-based systemsHealthcare AI services and solutions that can transform your operations, diagnostics, and patient care to reduce the burden on your staff and support your clinical decisions.
GDPR (General Data Protection Regulation)CCPA (California Consumer Privacy Act)HIPAA (Health Insurance Portability and Accountability Act)FISMA (Federal Information Security Management Act)EU Artificial Intelligence Act (AI Act)UAE Federal Data Protection Law (PDPL)Sector-specific data residency requirementsPDPA (Personal Data Protection Acts – APAC regions)State-level Privacy Laws (e.g., US State Privacy Acts)Cross-Border Data Transfer & Residency RequirementsExplainable AI (XAI) practicesAI model transparency & interpretability standardsAI algorithm testing & validation guidelinesModel governance & lifecycle managementNIST AI Risk Management FrameworkOECD AI Principles & Ethics GuidelinesEU Trustworthy AI PrinciplesIEEE standards for AI systems & applicationsBias detection & fairness evaluation practicesHuman-in-the-loop AI design principlesAI development costs can vary depending on your geographical location, project complexity, the tech stack used, and the level of customization needed.
$5,000 - $50,000
The service covers feasibility, validation, use-case analysis, planning, and early value estimation services. This gives you an overall view of the project and its scope before development.
1 - 2 months
$10,000 - $100,000
We coordinate with your teams to collect all your enterprise data and perform data cleaning operations on it. This is essential in preparing the data stream required for training the AI models.
2 - 4 months
Pre-Trained Model Adaptation $35,000 - $150,000
Custom Model Development And Training $50,000 - $200,000
Service costs can vary based on the chosen model, data volume, and accuracy targets. Adapting pre-trained models is faster and cost-efficient, while custom training is more suited for highly specialized use cases.
3 - 6+ months
$15,000 - $100,000
The final cost for integration & deployment depends on how AI connects to your ERP, CRM, data platforms, and workflows. We ensure a clean integration with all your existing systems before deployment.
2 - 3 months
Step 1
Identify your goals, constraints, and success metricsAssess AI use cases and validate its feasibilityConfirm data availability and integration pointsDefine your KPIs, risks, and expected ROIAlign stakeholders on scope and outcomesStep 2
Identify and connect relevant data sourcesClean up and structure all the raw dataAddress data gaps, quality issues, and biasDesign data pipelines and storage pointsEstablish data governance and access controlsStep 3
Explore data patterns and correlationsEngineer useful features and signalsReduce noise and irrelevant variablesValidate impact of features on outcomesPrepare datasets for model trainingStep 4
Select the correct model types and approachesDesign architectures based on performance needsBalance accuracy, latency, and cost constraintsChoose training and inference strategiesPlan deployment and scalability considerationsStep 5
Train models using curated datasetsTune parameters for optimal performanceEvaluate accuracy, precision, recall, and stabilityValidate against training and real-world dataDocument model behavior and assumptionsStep 6
Test models under edge and failure scenariosEvaluate robustness, fairness, and biasOptimize performance and inference efficiencyAdd explainability and confidence measuresPrepare models for production conditionsStep 7
Integrate models with existing systems and APIsDeploy across cloud, on-prem, or hybrid setupsImplement security and monitoring frameworksEnable real-time or batch inference pipelinesValidate end-to-end system performanceStep 8
Monitor model performance and driftTrack usage, latency, and cost metricsRetrain models as data patterns changeManage versions, rollbacks, and updatesMaintain governance and audit readinessAs good of an Artificial Intelligence development company as we are, we would not be able to engineer AI solutions without our tech partners.
AI products come alive when the correct engineering disciplines come together throughout the product lifecycle
Our AI development stack is the secret ingredient behind every product we deploy
Contact us today for your free AI consultation.
Contact us @sales@technodx.com
Need sale talks?+91 7994772996 / +1 5104459927
Contact us @sales@technodx.com
Need sale talks?+91 7994772996 / +1 5104459927
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We provide end-to-end AI services including strategy & roadmapping, data engineering, custom model development, generative AI systems, predictive analytics, computer vision, AI ops & governance, and integration with your existing systems.
We assess your current systems and processes to identify areas where AI can generate measurable business value. Signs include repetitive tasks, data-driven decisions, and growth barriers that AI could help optimize.
The choice depends on your business use case. LLMs excel in language, summarization, and reasoning tasks, while traditional ML works well for structured predictions and optimization. We can help you choose what best fits your business.
AI deployment timelines vary according to the project’s complexity. A small use case like a simple chatbot can go live in weeks, while a larger project like full system integration can take months. Most projects start delivering value early through a proof of concept, then scale step by step into production.
We align our AI development with your region’s data privacy laws (e.g., GDPR, CCPA, PDPA), embed governance and explainability best practices, and implement enterprise-grade security protocols to protect your data.
Yes, integration is a core part of our development process. We design secure APIs, event-driven pipelines, and connectors that let AI models operate within your current workflows without causing disruptions.
We evaluate your data, business goals, performance needs, and constraints to select the most suitable models for your business; this can be anything from generative AI and RAG pipelines to specialised vision or forecasting models.
Yes, we provide ongoing model monitoring, optimization, retraining, governance, and performance tracking to ensure long-term reliability and alignment with changing data and business priorities.
