From GenAI applications and AI Agents to intelligent automation, we build production-ready systems around your data, workflows, and technology stack.
CMMI
Level 3
HIPAA
Compliant
AWS
Partner Network
ISO Certified
For Data Quality & Security
SOC 2
Type II Compliant
We assess your data maturity, infrastructure, and business workflows to identify AI use cases with the strongest return. Our experts then produce a prioritized roadmap, build-versus-buy analysis, and AI governance plan.
Our AI engineers design, train, and fine-tune ML and DL models using PyTorch, TensorFlow, and scikit-learn. We then tailor feature engineering, model selection, and evaluation pipelines to your data and accuracy goals.
Our AI development company seamlessly integrates AI capabilities into your existing ERP, CRM, and line-of-business systems—including Salesforce, SAP, and Microsoft Dynamics. We do this using secure APIs, middleware, and event-driven connectors without disrupting daily workflows.
Access production-grade AI capabilities through managed, cloud-hosted services on AWS, Azure, or Google Cloud. Our AI developers handle model hosting, scaling, monitoring, and updates under a predictable engagement model.
We apply AI/ML to IT operations to automate anomaly detection, event correlation, and predictive incident management. Our AIOps solutions connect with observability tools like Datadog, Splunk, and Prometheus, so your IT teams spend less time chasing alerts.
Build role-specific AI copilots that assist sales, support, finance, and engineering teams within their daily applications. Our Copilots are grounded in your internal business knowledge through retrieval pipelines (RAG) and operate within existing access permissions.
We validate models before launch for accuracy, robustness, bias, and security using automated evaluation suites, red-teaming, and benchmark datasets. Our AI developers test all LLM outputs for hallucinations, prompt injection, and policy compliance.
Monitor deployed AI/ML models for data drift, performance decay, and bias using tools like MLflow, Kubeflow, and cloud-native MLOps tooling. We use automated retraining pipelines, version control, and rollback plans to keep models accurate as your business changes.
Some AI capabilities deserve dedicated expertise. We have hence created the following practices to address the use cases enterprises ask about most, each backed by focused teams, proven frameworks, and production experience.
Build AI agents that plan and execute multi-step tasks across your systems using LangGraph, CrewAI, and MCP. Guardrails and audit logs keep every action traceable while agents take over repetitive workflows.
Our generative AI development services cover LLM selection, fine-tuning, and RAG on your proprietary data, with built-in output validation. Scale content, document, and knowledge work without scaling headcount at the same rate.
Build NLP-powered chatbots and voice assistants for web, mobile, WhatsApp, and Teams, connected to your CRM and helpdesk. Smart escalation hands complex queries to human agents, so customers get faster answers at lower support costs.
Our AI architects will review your data, workflows, and goals,
then map the custom AI solutions most likely to pay off first.
AI regulations are moving fast, and non-compliance now carries real financial and reputational risk. Every AI solution we build is mapped to the frameworks that govern AI, data privacy, and security in your industry and region.
AI Risk Management
AI System Using Machine Learning
AI Concepts and Terminology
Bias in AI Systems
Robustness of Neural Networks
Governance Implications of AI
AI System Life Cycle Processes
Compliance is designed in from the first sprint, not retrofitted before an audit. This keeps launches on schedule and regulators satisfied.
Enterprise AI rarely runs on a single technique. Our engineers combine these specializations to solve problems that off-the-shelf tools cannot handle on their own.
Pair UiPath or Power Automate bots with machine learning to automate invoices, emails, and forms end-to-end.
Ground LLM answers in your verified company data with vector search and source citations for trustworthy results.
Apply image and video analytics for defect detection, OCR, and visual inspection to catch quality and safety issues earlier.
Turn contracts, tickets, and reviews into structured, searchable data with entity recognition and sentiment analysis.
Run optimized models on IoT and industrial devices for real-time decisions with lower latency and data kept on-site.
Make model decisions transparent with SHAP and LIME so business users, auditors, and regulators can trust them.
Deploy open-weight models on private or on-premises infrastructure for full control over data residency and access.
We reduce inaccurate AI outputs through RAG grounding, source citations, confidence scoring, and output validation layers.
We protect data with role-based access, end-to-end encryption, private networking, and PII redaction before data reaches any model.
We build regulatory controls into architecture from day one, including audit trails, model cards, and impact assessments.
We design approval checkpoints, escalation paths, and override controls for high-stakes decisions, with humans remaining accountable.
We architect model-agnostic systems with abstraction layers that let you switch LLM providers as pricing and performance change.
We ship every model with built-in monitoring, drift detection, versioning, and rollback using CI/CD/CT pipelines.
Solved an acute problem
A commercial lender's underwriters spent 20–30 hours per loan file on manual data entry. We built a custom-trained document processing and workflow platform on AWS.
35%
higher throughput
99.5%
extraction accuracy
Rebuilt a broken core system
A bath & lighting brand in 55+ countries had site search converting just 10–15% of searchers. We trained a transformer ranking model on their own catalog and query logs.
80%
more accurate search
72%
higher conversion
Found the bigger problem
An eCommerce retailer brought us in for nopCommerce upkeep. The real bottleneck was inventory and pricing scattered across five channels — so we built the fix.
60%
increase in sales
~90%
tasks automated
Provided a dedicated engineering pod
A core retail POS platform had to outgrow three technology eras without losing what made it work. The same embedded pod carried it from CGI to PHP to AWS over 15 years, so no knowledge was ever lost in handover.
15+ years
Zero re-tendering and handovers
The cost depends on your use case complexity, data readiness, model choice, integration scope, and compliance needs. Typically, a focused proof of concept costs far less than an enterprise-wide AI platform. TechnoScore can provide a scoped estimate after a discovery session, so your budget matches expected business value. Reach out to us at info@technoscore.com.
Timelines vary with scope, data quality, and integration depth. A proof of concept usually moves faster than a production rollout with compliance reviews. We deliver in phases, so you see working results early and can refine before scaling.
Our AI engineers ground model responses in verified company data through retrieval-augmented generation (RAG). Source citations, confidence thresholds, output validation, and automated evaluation further reduce inaccurate answers. High-stakes outputs can also route to human reviewers before reaching users.
Most enterprises get the best value by adapting an existing LLM with RAG, prompt engineering, or fine-tuning. Custom models make sense for specialized data, strict data residency, or cost control at high volume. We can benchmark both options on your data before recommending one.
We follow a zero-trust security model with encryption, role-based access, private deployments, and PII redaction. Our solutions align with frameworks such as GDPR, HIPAA, SOC 2, ISO/IEC 42001, and the EU AI Act. We never use your data to train third-party models without consent.
Yes. We can integrate AI capabilities with platforms like Salesforce, SAP, Microsoft Dynamics, and custom legacy applications through APIs and middleware. This lets teams use AI inside familiar tools without replacing core systems.
You retain full ownership of all code, trained models, prompts, configurations, and data produced during the engagement. We document everything and hand it over so your internal team can run, modify, or extend it independently.
Project initiation within 48 hours
Round-the-clock support
Strict NDAs for data security
Share your project requirements.
