AI Engineering · Toronto, Canada

We build production AI systems that ship.

From data foundations to fine-tuned models, inference infrastructure, and AI agents — end-to-end, engineering-led, measurably effective.

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Our capabilities

LLM Engineering AI Inference Infrastructure Model Fine-Tuning RAG Systems AI Agents MLOps & LLMOps Data Engineering Evaluation Frameworks Observability AWS Bedrock Databricks Snowflake vLLM Hugging Face PyTorch LangGraph Pinecone LLM Engineering AI Inference Infrastructure Model Fine-Tuning RAG Systems AI Agents MLOps & LLMOps Data Engineering Evaluation Frameworks Observability AWS Bedrock Databricks Snowflake vLLM Hugging Face PyTorch LangGraph Pinecone

VedhaAI is an AI engineering company. We design and ship production machine learning systems across LLM infrastructure, retrieval-augmented generation, model fine-tuning, inference optimization, AI agents, and enterprise data platforms.

We are engineers and researchers who have spent a decade building real AI systems in production: 50+ deployed models, enterprise-scale data platforms, real-time inference services, and applied LLM products across financial services, retail, and technology. We have debugged the failures and learned what actually works.

We do not make a distinction between strategy and implementation. We architect and we build. We write the code, run the evals, and watch the dashboards after deployment. Our clients trust us because we understand the full stack — from raw data pipelines to model weights to the APIs that serve predictions.

Our goal with every engagement is the same: a system that works in production, can be observed and measured, and can be understood and maintained by the team who inherits it.

01 LLM Engineering & Generative AI Production LLM pipelines, prompt systems, structured outputs, multi-model orchestration, safety layers, and API integration.

02 AI Inference Infrastructure Low-latency, high-throughput model serving with vLLM, TGI, Triton, quantization, and GPU cost optimization.

03 Model Fine-Tuning & Post-Training Domain adaptation via LoRA, QLoRA, SFT, DPO, and RLHF — with rigorous evaluation before and after training.

04 Retrieval-Augmented Generation Hybrid retrieval, reranking, semantic caching, knowledge graphs, and hallucination-aware output pipelines.

05 AI Agents & Workflow Automation Agentic architectures with tool use, memory, planning, and reliable integration into enterprise systems and APIs.

06 MLOps, LLMOps & Evaluation Training pipelines, model registries, eval frameworks, drift detection, tracing, and production observability.

07 Data Engineering for AI Lakehouse architectures, feature stores, streaming pipelines, and data quality systems built for ML consumption.

Enterprise · RAG

Enterprise Knowledge Retrieval Platform

Hybrid dense-sparse retrieval over 5M+ documents with cross-encoder reranking, semantic caching, and hallucination guardrails. Sub-300ms P95 latency in production.

↑ 73% reduction in support ticket volume. 91% answer accuracy on held-out eval set.

FinTech · Fine-Tuning

Domain-Adapted Financial Analysis LLM

LoRA fine-tuned 7B model on proprietary financial corpus with RLHF alignment for citation-grounded, compliance-safe output. Deployed on SageMaker with CI/CD eval gates.

↑ 4.2× improvement over GPT-4 baseline on domain benchmark suite.

Retail · MLOps

Unified ML Inference Platform

Centralised inference layer on AWS serving 50+ production models with autoscaling, canary deployments, and real-time drift monitoring integrated with Snowflake.

↓ 60% infrastructure cost. 99.97% uptime across 18 consecutive months.

Technical Depth

The tools we work with daily

LLM & Model Layer

PyTorch

Hugging Face

PEFT / LoRA

TRL

Axolotl

DeepSpeed

vLLM

TGI

Triton

ONNX Runtime

LangChain

LangGraph

Cloud & Infrastructure

AWS Bedrock

SageMaker

Azure AI Studio

Kubernetes / EKS

Terraform

Data & Platform

Databricks

Snowflake

Apache Spark

Apache Kafka

dbt

Airflow

Delta Lake

Vector & Search

Pinecone

Weaviate

Qdrant

pgvector

OpenSearch

Observability

MLflow

Weights & Biases

Langfuse

Arize AI

Evidently

Careers

We are hiring

We're selectively building a team of ML engineers, research scientists, and AI infrastructure builders who want to work on hard problems in applied AI. We hire for depth, production experience, and intellectual honesty — not keyword matching.

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We build production AI systems that ship.

What we build

Systems we have shipped

The tools we work with daily

Technical notes & thinking

We are hiring