AI & Machine Learning

AI & Machine Learning for Prediction, Vision & Automation

AI is now a competitive advantage. Tekvers helps businesses automate intelligently, predict accurately, and personalize experiences through practical production-ready machine learning—NLP, computer vision, forecasting, and agents.

For generative AI copilots, RAG, and LLM product features, see our dedicated Generative AI & LLM Integration service. This page focuses on classical ML, vision, NLP pipelines, and intelligent automation wired into your stack.

What We Deliver

Concrete capabilities included in a typical ai & machine learning engagement.

• LLM integration using OpenAI, Claude, and Gemini APIs
• AI agents and multi-step workflow automation
• RAG systems, semantic search, and knowledge assistants
• Custom ML models for prediction, classification, and regression
• NLP solutions including chatbots and document intelligence
• Computer vision for OCR, recognition, and object detection
• Intelligent automation and AI-powered semantic search
• Data pipelines for ETL, feature prep, and model operations
• AI evals, guardrails, cost monitoring, and production rollout

Why Tekvers

• Engineering-led delivery—not prompt-only experiments
• Production focus: security, evals, logging, and fallbacks
• Experience with booking parsers, document automation, and LLM copilots
• Integrates with Next.js, .NET, Node.js, and cloud stacks you already run

Our Process

A structured delivery model from discovery through launch and ongoing support.

1. Use-case discovery

   We map business goals, data availability, compliance constraints, and success metrics before choosing models or architecture.

2. Prototype & evaluate

   Rapid proof-of-value with eval datasets, prompt/version control, and human review loops—typically 4–8 weeks.

3. Production integration

   APIs, queues, dashboards, and observability wired into your existing web apps, ERP, or mobile products.

4. Monitor & improve

   Ongoing model monitoring, drift detection, cost tracking, and iteration as your data and use cases evolve.

Technologies We Use

OpenAI API, Anthropic Claude, Google Gemini, LangChain, Hugging Face, TensorFlow, PyTorch, Scikit-learn, Python, Node.js, PostgreSQL, Redis, vector databases, AWS SageMaker, and Azure AI services.

Relevant Case Studies

Production projects from our portfolio that demonstrate how we deliver ai & machine learning for real businesses.

• 

  Multi-tenant venue booking SaaS

  Velay

  Challenge: Sports venues needed booking, live floor operations, payments, inventory, and customer channels in one system—but existing tools were generic SaaS or offline spreadsheets that could not handle multi-tenant isolation, Pakistan payment reality, or realtime facility views.

  Solution: Velay: Velay—Pakistan sports & gaming venue SaaS with live court ops, map booking, Pakistan payments, and multi-surface realtime. End-to-end booking → live session → checkout loop across App, Pro, Dashboard, and API. Tekvers delivered a maintainable multi-tenant venue booking saas system for Velay using NestJS, PostgreSQL, Redis, Expo, with phased rollout, operator workflows, and documentation suited to long-term ownership.

  • End-to-end booking → live session → checkout loo…
  • Broad admin/consumer API surface for venues, fac…
  • Tenant-isolated multi-location operations with l…

  Read case studyVisit live site

  vellay.app

• 

  Solana wallet & trading ecosystem

  DEGN DApp

  Challenge: Solana traders needed custodial wallet UX, spot swaps, portfolio balances, referrals, and ops tooling in one product family instead of fragmented third-party tools—with mobile and admin surfaces on a shared API. Without a coherent solana wallet & trading ecosystem foundation, DEGN DApp stakeholders faced fragmented tools, slow handoffs, and limited visibility—classic failure modes Tekvers designs against.

  Solution: DEGN DApp: Multi-client Solana wallet and trading platform—NestJS/MongoDB APIs, Turnkey custodial wallets, Jupiter spot trading, admin dashboard, and Android/React Native clients. Cohesive Solana wallet/trading product family live around degn.app. Tekvers delivered a maintainable solana wallet & trading ecosystem system for DEGN DApp using NestJS, MongoDB, Next.js, React, with phased rollout, operator workflows, and documentation suited to long-term ownership.

  • Cohesive Solana wallet/trading product family li…
  • Custodial UX with portfolio balances and referra…
  • Ops tooling for token catalog and market monitor…

  Read case studyVisit live site

  degn.app

• 

  Multi-package UK transfer booking platform

  Taxi App Platform

  Challenge: A UK transfer operator needed online booking, fleet/pricing configuration, composite fares, and payment initiation across admin and multiple rider UIs—without claiming live notification delivery or a driver app the repo does not contain.

  Solution: Taxi App Platform: ASP.NET Core 8 + SQL Server booking API with layered GBP fare engine, React ops admin, and parallel React/Next.js rider sites—Stripe and PayPal payment initiation. Multi-package UK transfer booking and dispatch foundation. Tekvers delivered a maintainable multi-package uk transfer booking platform system for Taxi App Platform using ASP.NET Core 8, EF Core, SQL Server, JWT, with phased rollout, operator workflows, and documentation suited to long-term ownership.

  • Multi-package UK transfer booking and dispatch f…
  • Composite fare calculation with documented rule …
  • Admin fleet and pricing configuration without a …

  Read case studyVisit live site

  airporttransferstaxi.co.uk

Frequently Asked Questions