Azguards builds generative AI features directly into your products and workflows:
text generation, image synthesis, voice interfaces, custom fine-tuned models, and multimodal pipelines.
We work with Fable, Sonnet, Haiku, OpenAI reasoning models, Gemini Flash, DeepSeek, LLaMA, and HuggingFace
models, and we don't stop at MVP.
An AI-powered shopping agent built on the LangChain, LangGraph, and LangSmith full-stack with Next.js and React.js, deployable across web stores and integrated with HubSpot CRM. The system vectorises product catalogs into Qdrant, leverages Jina Reranker for high-precision retrieval, and persists relational data in PostgreSQL to enable hyper-personalized recommendations and automated conversational workflows.
A multi-stage content pipeline that drafts, validates, and routes long-form marketing content
with editor-in-the-loop review at each handoff. Case study in works.
Designed and built an automated asset creation pipeline for a leading GCC eyewear brand, replacing external photography vendors. Inputting just 2 base product photos automatically generates studio-grade shots from 5+ angles and realistic model renders.
~90%
Asset Cost Reduction
< 1 min
Turnaround (was days)
✓Automated Pipeline: 5+ studio angles & realistic model renders from 2 base photos
Every engagement starts from one of these five building blocks. Most projects combine two or three.
01
LLM integration & API development
Connect Fable, Sonnet, Haiku, OpenAI reasoning models, or Gemini Flash to your product via clean, maintainable APIs. We handle prompt engineering, context management, streaming responses, token budgets, and cost optimisation so your team inherits a system they can actually maintain.
LangchainFastAPILLMsPydantic
02
Custom model fine-tuning
Take a capable open-weights base model (LLaMA, DeepSeek, Mistral, Qwen) and train it on your data. The result speaks your brand voice, understands your domain terminology, and runs on your own infrastructure. No data leaves your environment.
HuggingFaceTransformersPyTorchLoRA / QLoRAAWS
03
Multimodal AI: image, voice, document
Image classification, voice transcription, document extraction, and visual search. We design pipelines that combine seeing, listening, and reading with structured output and human review checkpoints baked in.
OpenCVWhisperLibrosaNLTKElevenLabsPyTorch
04
AI-powered search & recommendation
Semantic search that understands intent, not just keywords. Recommendation engines that learn from behaviour over time. Deployed on your existing product catalog, content library, or dataset without a platform migration.
Product description generators, email drafters, SEO content pipelines, internal report writers. Built with structured output, tone controls, and human review checkpoints, baked in from day one.
LangchainFastAPILLMsPostgreSQL
MODEL SELECTION
We don't default to one model. Here's how we choose.
The right model depends on your use case, data sensitivity, latency requirements, and budget. We document the trade-offs so your team understands the decision — not just the outcome.
Claude · Fable, Sonnet, Haiku & Opus
Anthropic
REASONING
Our default for complex reasoning, multi-step agentic workflows, and structured document analysis. Exceptional instruction-following with minimal hallucination on constrained tasks.
Preferred for deep step-by-step logic, function calling, tool use, and structured output generation. Mini variants add ultra-fast execution for high-throughput pipelines.
Built for million-token context windows and native multimodal processing (video, audio, code, and images). Natural choice for media-heavy workflows and Google Cloud integrations.
Best for
Long-context document analysis, native video & audio processing, multimodal RAG.
DeepSeek · LLaMA · Mistral · Qwen
Open weights & Self-hosted
SELF-HOSTED
When data cannot leave your infrastructure, cost per token is a key constraint, or you need specialized fine-tuning. We deploy, fine-tune, and host these on your VPC or on-prem.
Best for
Data privacy compliance, high-volume cost optimization, custom domain fine-tuning.
OpenRouter & Hybrid Routing
Routing layer
ROUTING
A model-agnostic routing system that dynamically routes requests (e.g. Sonnet vs Flash vs local DeepSeek) based on task complexity, budget, and real-time provider availability.
The most common question we get on a first call: "Should we fine-tune, use RAG, or just engineer better prompts?" The answer depends on what you're actually trying to change. Here's how we think about it.
Prompt engineering
The model already knows enough: you just need to direct it precisely. We build reusable, versioned prompt libraries with structured output parsers.
INVESTMENT
Low
TIME
< 1 week
COMPLEXITY
■■■
Fast to implement. No training required. Degrades if the model is updated.
CHOOSE THIS WHEN
Choose this when the task is well-scoped and consistent, and the model's base knowledge is sufficient.
Fine-tuning
The model's behaviour needs to change, not just its answers. Brand voice, domain-specific reasoning, custom output format that prompts alone can't enforce.
INVESTMENT
High
TIME
6–10 weeks
COMPLEXITY
■■■
Takes longer and requires quality training data. But the result is a model that thinks the way you need it to.
CHOOSE THIS WHEN
Choose this when consistency across thousands of outputs matters more than flexibility.
RAG
Grounds AI answers in your specific data (documents, catalogs, databases, knowledge bases). No training required. Data stays current without retraining.
INVESTMENT
Medium
TIME
4–6 weeks
COMPLEXITY
■■■
Citable answers from your data. Stays current without retraining. Requires a search infrastructure.
CHOOSE THIS WHEN
Choose this when you have proprietary data that changes frequently and need accurate, verifiable answers from it.
HOW WE WORK
A method we follow on every project.
Five stages, in order. Skipping any of them is how Gen AI projects end up as PoCs that never make it to production.
01
STAGE 1 / 5
Use-case scoping
We define the AI task precisely before touching a model: input type, expected output format, latency tolerance, accuracy floor, and what 'wrong' looks like. A vague brief produces a vague system. We don't start building until we're both clear on the success criteria.
MULTIMODAL SYSTEMS
Beyond text: image, voice, and document AI.
Most teams think 'Gen AI' means text generation. Our stack covers three modalities, enabling us to build systems that require seeing, listening, and reading, not just writing.
01 · image
Image AI
Product image classification, visual defect detection, AI image generation via Midjourney API or Stable Diffusion, and visual search that matches products by appearance rather than keywords.
STACK
OpenCVCLIPPyTorchStable Diffusion
02 · voice
Voice AI
Speech-to-text transcription at scale, voice-commanded interfaces, audio classification, and keyword spotting for compliance or moderation use cases.
STACK
WhisperLibrosaNLTKFastAPI
03 · document
Document AI
Intelligent document processing that extracts structured data from PDFs, invoices, contracts, and forms. Combines OCR with LLM-powered field extraction and validation.
STACK
TransformersFastAPIPostgreSQLAWS S3
READY TO BUILD?
Have a Gen AI feature in mind? Let's scope it.
Book a 30-minute architecture call with our team. We'll discuss your use case, data constraints, and latency targets, and tell you honestly if Gen AI is the right solution.
01What's the difference between using an API directly and building a custom Gen AI system?
Calling an API gets you raw model output. A production system handles prompt versioning, structured output validation, fallback chains, cost and rate-limit handling, retrieval/grounding, evaluation, and observability.
02Do you work with open-source models or only commercial ones?
Both. We default to whichever serves your constraints best. Commercial model suites (Claude Fable/Sonnet/Haiku, OpenAI o-series/Omni, Gemini Flash/Pro) are usually fastest to ship with; open-weights (DeepSeek, LLaMA, Mistral, Qwen) make sense when data sensitivity, cost-at-scale, or fine-tuning are primary constraints.
03How do you handle hallucinations and output quality?
Structured output schemas (Pydantic) so malformed answers are rejected before they reach users; RAG grounding so the model answers from your data; eval harnesses run on every prompt change; and human-in-the-loop checkpoints on high-stakes outputs.
04Can you integrate Gen AI into our existing product without a full rebuild?
Yes, most of our work is incremental. We ship as an API your existing backend calls, or as a microservice your frontend talks to directly. No platform migration, no rip-and-replace.
05How do you prevent unexpected API cost spikes and token inflation?
We engineer multi-tier model routing (e.g. running low-cost classification via Haiku or Flash before invoking Sonnet), implement response caching for recurring queries, set per-session token budgets, and build real-time telemetry dashboards.
06Who owns the custom codebase, fine-tuned weights, and pipeline architecture?
You retain 100% ownership of all custom code, fine-tuned model checkpoints, vector store schemas, and pipeline infrastructure. We deliver fully documented Git repositories and conduct structured technical handovers with your team.
07What happens when model providers release updates or deprecate API versions?
We build automated evaluation harnesses that run regression tests across your prompts whenever a provider updates a model. We also offer maintenance retainer options for continuous telemetry monitoring, latency optimization, and version upgrades.
