Generative AI & LLM Integration

Generative AI & LLM Integration for Real Products

Generative AI only creates value when it ships inside workflows your team already runs—support, search, content ops, and in-app copilots—not as a disconnected chatbot demo.

Tekvers integrates OpenAI, Claude, and Gemini with RAG over your documents, prompt/version control, evals, guardrails, and cost monitoring so LLM features stay reliable in production.

What We Deliver

Concrete capabilities included in a typical generative ai & llm integration engagement.

• LLM API integration (OpenAI, Anthropic, Google Gemini)
• RAG systems with vector search over proprietary documents
• In-app copilots, chat, and knowledge assistants
• Prompt versioning, eval harnesses, and safety guardrails
• Usage metering, caching, and cost controls
• Secure data handling and human-in-the-loop review paths

Why Tekvers

• Engineering delivery with evals—not prompt-only experiments
• Proven voice and agent work (Zallo) informs production LLM patterns
• Integrates into Next.js, NestJS, and .NET products you already own
• Clear separation from classic ML so buyers get a focused landing page

Our Process

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

1. Use-case & data fit

   We define the job-to-be-done, retrieval sources, privacy constraints, and success metrics before writing prompts or wiring models.

2. Prototype with evals

   Ship a thin slice with golden questions, failure modes, and human review—so quality is measured, not assumed.

3. Production hardening

   Auth, rate limits, observability, fallbacks, and cost budgets land before broad rollout.

4. Iterate on outcomes

   Monitor deflection, latency, and hallucination rates; expand retrieval and tools as the product proves value.

Technologies We Use

OpenAI, Anthropic Claude, Google Gemini, LangChain, vector databases, NestJS, Next.js, Python services, PostgreSQL, Redis, and cloud AI APIs on AWS/Azure.

Relevant Case Studies

Production projects from our portfolio that demonstrate how we deliver generative ai & llm integration for real businesses.

• 

  Multi-product enterprise suite

  Zallo

  Challenge: Appointment businesses lose bookings when phones go unanswered; operators also need modular back-office SKUs instead of a single legacy monolith release train. Without a coherent multi-product enterprise suite foundation, Zallo stakeholders faced fragmented tools, slow handoffs, and limited visibility—classic failure modes Tekvers designs against.

  Solution: Zallo: Gateway-led suite—production multi-tenant AI receptionist (zallo.ai) plus NestJS/Next ERP-style SKUs (CRM, POS, finance, HRM, inventory, CMS) with shared portal UI. End-to-end receptionist loop: answer → book → SMS follow-up. Tekvers delivered a maintainable multi-product enterprise suite system for Zallo using NestJS, Next.js, TypeScript, Supabase Postgres + RLS (REC), with phased rollout, operator workflows, and documentation suited to long-term ownership.

  • End-to-end receptionist loop: answer → book → SM…
  • Independent module delivery with shared identity…
  • Clearer commercial packaging per Zallo product S…

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  zallo.ai

• 

  Voice AI product

  AI Voice Receptionist

  Challenge: SMBs needed always-on inbound call handling that books and answers from real business data—while a platform team needed safe multi-tenant onboarding, billing, and ops tooling. Without a coherent voice ai product foundation, AI Voice Receptionist stakeholders faced fragmented tools, slow handoffs, and limited visibility—classic failure modes Tekvers designs against.

  Solution: AI Voice Receptionist: Zallo Receptionist—multi-tenant AI phone agent with NestJS APIs, Supabase, Vapi/Twilio, Stripe billing, and dual Next.js portals. Always-on front-desk model that books against real availability and FAQs. Tekvers delivered a maintainable voice ai product system for AI Voice Receptionist using NestJS, Next.js, Supabase, TypeScript, with phased rollout, operator workflows, and documentation suited to long-term ownership.

  • Always-on front-desk model that books against re…
  • Centralized tenant lifecycle for platform operat…
  • Monetization path via Stripe plans and feature e…

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• 

  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…

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  vellay.app

Frequently Asked Questions