AI System Development
Accepting Projects

AI That Works
In Production
Not Just in Demos

We design, train, and deploy AI systems that solve real business problems — LLM applications, autonomous agents, RAG pipelines, computer vision, and custom ML models. Built to scale, monitored in production, engineered to last.

🧠LLM Integration
📚RAG Systems
🤖AI Agents
👁️Computer Vision
50+
AI Systems Shipped
99.1%
Production Uptime
< 200ms
Avg. Response Time
FORWARD PASS / NEURAL INFERENCEINPUT5 nodesHIDDEN6 nodesHIDDEN6 nodesOUTPUT3 nodes
inference.log
tokens/sec1,248
latency138 ms
accuracy97.3%
model: gpt-4o / claude-3-5
GPT-4o · Claude · Gemini
Real-time Inference
🔒Private & Secure
Our AI & ML Stack
OpenAI GPT-4o
Anthropic Claude
Google Gemini
Meta Llama 3
Mistral
LangChain
LlamaIndex
HuggingFace
PyTorch
TensorFlow
Pinecone
Weaviate
ChromaDB
FastAPI
Celery
Redis
RLHF
RAG
LoRA Fine-tuning
FAISS
LangGraph
CrewAI
OpenAI GPT-4o
Anthropic Claude
Google Gemini
Meta Llama 3
Mistral
LangChain
LlamaIndex
HuggingFace
PyTorch
TensorFlow
Pinecone
Weaviate
ChromaDB
FastAPI
Celery
Redis
RLHF
RAG
LoRA Fine-tuning
FAISS
LangGraph
CrewAI
What We Build

Six Classes of
AI Systems

LLM Apps
🧠

LLM-Powered Applications

From document summarisation to intelligent copilots — we build production LLM apps with prompt engineering, context management, streaming responses, and guardrails that keep outputs safe and on-brand.

RAG
📚

RAG & Knowledge Systems

Retrieval-Augmented Generation that actually retrieves the right content. We design chunking strategies, embedding pipelines, hybrid search, and re-ranking so your AI answers from facts, not hallucinations.

Agents
🤖

Autonomous AI Agents

Multi-step reasoning, tool use, memory, and planning. We build agents that can browse the web, run code, call APIs, and complete complex workflows with minimal human intervention.

Vision
👁️

Computer Vision

Object detection, OCR, defect inspection, medical imaging, and video analytics. We fine-tune vision models on your data and deploy them at the edge or in the cloud with sub-100ms latency.

MLOps
⚙️

ML Infrastructure & MLOps

Training pipelines, experiment tracking, model registry, A/B deployment, drift monitoring, and auto-retraining. We wire the full ML lifecycle so your models improve over time without manual intervention.

Fine-tuning
🎯

Custom Model Fine-tuning

Domain-specific models that outperform GPT-4 on your exact task at 10× lower cost. We manage dataset curation, LoRA/QLoRA training, RLHF alignment, and quantised inference deployment.

50+
AI Systems in prod
across 12 industries
99.1%
Uptime SLA
across all deployments
< 200ms
Avg. inference time
measured at P95
4.2×
Avg. ROI delivered
within 6 months of launch
Live Demo

Watch AI
Think & Respond

Every system we build follows the same architecture: understand context, reason through steps, take actions, and stream results in real-time. No black boxes. Full observability at every layer.

🔍
Retrieval-grounded answers
Every response cites sources from your data.
🔄
Multi-step reasoning chains
Complex tasks broken into auditable steps.
Token streaming by default
Users see results immediately, not after 8s waits.
blenvo-ai — terminal
LIVE
user@blenvo:~$ Summarise all support tickets from last week and identify the top 3 root causes
_Initialising AI pipeline...
Our Process

12 Weeks.
Idea to AI in Production.

Week 1–2

Discovery & AI Strategy

We map your data landscape, define the AI use case, benchmark existing solutions, and select the right model architecture. You get a detailed technical spec before any code is written.

🔭
Week 3–4

Data Pipeline & Preparation

Data collection, cleaning, annotation, and vector indexing. We build the ETL pipeline that feeds your AI with the right context — structured, unstructured, or both.

🗄️
Week 5–7

Model Development & Training

Prompt engineering, RAG pipeline assembly, or fine-tuning runs. Every experiment is tracked in MLflow. You see eval metrics after each iteration before we proceed.

⚙️
Week 8–9

Integration & API Layer

We wrap the model in a production-grade API with auth, rate limiting, caching, and streaming. Frontend SDKs or webhooks are provided for your engineering team.

🔗
Week 10–11

Evaluation & Red-teaming

Automated eval suites, adversarial prompt testing, bias audits, and latency benchmarks. We measure accuracy, hallucination rate, and cost per query before declaring production-ready.

🧪
Week 12

Deployment & MLOps Setup

Blue-green deployment, auto-scaling, observability dashboards, cost monitoring, and alert setup. We hand off runbooks and stay on as your on-call ML team for 30 days.

🚀
Ongoing

Monitoring & Iteration

Drift detection, ground-truth collection, prompt versioning, and scheduled retraining. We run monthly eval reviews and push improvements without disrupting production.

📈
Capabilities

What Goes Into
Every AI System We Ship

Answers from Your Data.

We build RAG pipelines that retrieve the right chunks every time — hybrid BM25 + vector search, contextual compression, re-ranking, and citation injection so every answer is grounded and auditable.

Hybrid search: BM25 + dense vector recall
Contextual chunk compression & re-ranking
Multi-doc reasoning with citation trails
Incremental indexing — no full re-index on updates
📄 Docs✂️ Chunk🧲 Embed🗃️ VectorDB🔍 Similarity Query🧠 LLM + Retrieved ContextGrounded answer with citationsRAG Pipelineembedding
Model Integrations

We Work With
Every Major LLM

We're model-agnostic. We select the right model for your task, latency budget, and data privacy requirements — or run several in parallel with intelligent routing.

🤖
GPT-4o
OpenAI
ReasoningCodingMultimodal

Best for complex reasoning & broad tasks

💜
Claude 3.5
Anthropic
Long ContextWritingSafety

Best for document analysis & nuanced outputs

Gemini 1.5
Google
1M ContextMultimodalSpeed

Best for massive document ingestion

🦙
Llama 3.1
Meta (Open)
Open-sourceFine-tunablePrivate

Best for private deployment & fine-tuning

🌪️
Mistral
Mistral AI
EfficientEU-compliantEdge

Best for cost-sensitive & edge workloads

🔀
Intelligent Model Router
Route each request to the optimal model based on task type, cost, and latency SLA
↓ 60%
Cost
↓ 42%
Latency
↑ 18%
Quality
Use Cases

Real AI.
Real Industries.

Transformative AI doesn't come from applying the same model to every problem. These are the use cases we've shipped — each with a different architecture chosen for the specific constraints.

Legal
Contract review & risk flagging

A top-5 law firm needed paralegals to review 200-page contracts in minutes. We built a RAG system over their clause library, trained a risk classifier on 8 years of litigation outcomes, and delivered a copilot that flags anomalies with cited precedents. Review time dropped from 6 hours to 22 minutes.

Retail
AI-powered product recommendation
Healthcare
Clinical note summarisation
Finance
Real-time fraud signal detection
SaaS
Autonomous customer support agent
Why Blenvo

What Sets Our AI
Practice Apart

🔬

Research-Backed Engineering

Our ML engineers publish and follow state-of-the-art research. We evaluate new techniques like speculative decoding, mixture-of-experts, and RAG-fusion before recommending them to clients.

🔭

Full-Stack AI Ownership

We own the data pipeline, the model, the API, and the monitoring. No fragmented vendors. One team responsible for the entire system — which means faster debugging and coherent architecture decisions.

📏

Evaluation-First Development

We define evaluation benchmarks before writing a single prompt. Every model version is compared to a human-graded gold set. You always know exactly how good your AI is, quantifiably.

🛡️

Safety & Guardrails Built In

Output classifiers, prompt injection detection, PII redaction, hallucination scoring, and configurable safety filters are standard in every system we ship — not optional add-ons billed separately.

Deployment

Deploy Where
Your Data Lives

☁️
Cloud API
Fastest time-to-value
OpenAI / Anthropic / Google hosted models
Serverless auto-scaling
Pay-per-token cost model
Deployed in days, not weeks
🔒
Private Cloud
Your VPC, your data
Deployed in your AWS / GCP / Azure VPC
Zero data egress to model providers
SOC2 & ISO 27001 ready architecture
Dedicated GPU instances for latency control
🏛️
On-Premise
Full data sovereignty
Air-gapped deployment on your hardware
Open-source models (Llama / Mistral)
No internet dependency for inference
Required for classified / regulated data
Edge AI
Device-side inference
Quantised models on mobile / IoT devices
ONNX / CoreML / TensorFlow Lite
Works offline with no network
Sub-5ms latency for real-time use cases
Ready to Build?

Your AI System.
In Production
In 12 Weeks.

Book a free AI scoping call. We'll review your use case, audit your data readiness, recommend an architecture, and give you an honest assessment of what's achievable and at what cost.

✅ Free technical audit✅ NDA on day one✅ Fixed-price milestones