Aizaz
LLM App Development
Ship LLM powered features and standalone apps with the retrieval, auth, monitoring, and UX your users and investors expect.
- Senior-led
- Production-minded
- Built to hand over
The engagement
From a real constraint to a system that works.
The problem
You want LLM features in your product but prototypes feel slow, hallucinate, and lack proper auth and billing. Wrapping ChatGPT in a UI is not a product — and investors and customers can tell.
How we help
We build LLM applications with production foundations: retrieval pipelines, prompt orchestration, user permissions, usage tracking, and monitoring. Whether it is an internal copilot or a customer facing AI feature, we ship software your team can maintain.
What we can own
Senior execution across the critical path.
- 01
Custom LLM application architecture
↗ - 02
Retrieval augmented generation and vector search
↗ - 03
Prompt orchestration and model routing
- 04
User auth, roles, and usage metering
- 05
Streaming UI and conversation interfaces
↗ - 06
Evaluation pipelines and quality benchmarks
- 07
Cost controls and latency optimization
Where it creates value
Built around real operating scenarios.
SaaS product → embed AI assistant → user scoped data → billing tier
Legal team → document copilot → clause search → draft suggestions
Sales team → proposal generator → CRM context → approval workflow
Research platform → semantic search → summarization → export reports
Why Aizaz Studio
Built for momentum without creating tomorrow’s mess.
Product-grade interaction design
Streaming, citations, editable outputs, feedback, and graceful fallback are designed around the user’s actual task.
Tenant-aware data access
Authentication, permissions, retrieval, and tool calls respect the same account boundaries as the rest of the product.
Quality, latency, and cost controls
Evaluation sets, tracing, caching, model routing, and usage budgets make the feature measurable and operable.
When to build an LLM application
A dedicated LLM app makes sense when the model-driven task is the product experience: document analysis, research, drafting, support, or a domain-specific copilot. If AI is only one feature inside an existing product, an integration project may be the better scope.
We test whether retrieval, tool use, or conventional search can solve the job before adding autonomous behavior.
Core components of a production LLM app
The application layer still needs normal product engineering: identity, tenancy, billing where relevant, data models, background work, APIs, and an interface that helps users inspect and correct results.
The AI layer adds prompt and schema versioning, scoped retrieval, model routing, evaluations, tracing, moderation where needed, and fallbacks for timeouts or weak answers.
Moving from a prototype to a dependable product
A prototype proves that a model can produce a useful result. Production work proves the feature can do it for different users, data, and edge cases within acceptable latency and cost. Representative evaluation data and a measured rollout close that gap.
Implementation guide
The model is one dependency inside a larger product
Our AI integration guide covers retrieval, tool calling, structured outputs, evaluations, observability, latency, and the controls needed beyond a prototype.
How we deliver
A clear path from context to production.
- 01
Define the product contract
Specify users, tasks, source data, output format, quality bar, latency, cost, and permission boundaries.
- 02
Build an evaluated vertical slice
Implement one end-to-end workflow with real data, retrieval or tools, UX, and repeatable evaluations.
- 03
Operationalize the feature
Add tracing, feedback, fallbacks, budgets, security review, and staged rollout before expanding scope.
FAQs
Frequently Asked Questions
Do you build standalone LLM apps or features inside existing products?+
Both. We ship standalone tools and embed LLM capabilities into existing web apps and SaaS platforms.
How do you reduce hallucinations in production?+
Retrieval grounding, structured outputs, validation layers, and evaluation suites tuned on your real data — not generic prompt tricks.
Can we use our own documents and databases as context?+
Yes. We build ingestion pipelines from PDFs, wikis, databases, and APIs so the LLM answers from your authoritative sources.
How do you manage LLM API costs at scale?+
Caching, model routing, token budgets, and usage metering so costs stay predictable as adoption grows.
What stack do you typically use for LLM apps?+
React or Next.js frontends, Node or Python backends, vector databases, and cloud deployment on AWS with standard observability tooling.
Next step
Turn the prototype into a product plan
Share the user task, source data, quality bar, and current prototype. We will outline the production gaps. hello@aizaz.studio +92 334 2056691