Service

AI Integration

LLM-powered features, RAG pipelines, and intelligent automation embedded directly into your product workflow.

We evaluate accuracy before deployment — not after
Full stack AI: from data ingestion to the user-facing feature
Every AI system includes monitoring, drift detection, and fallback logic
We've shipped real AI products — not just RAG demos

What's included

LLM integration
RAG pipelines
AI agents
Data extraction

Typical Engagement

$15,000 – $80,000

4–12 weeks

Get Started

AI features are only valuable if they actually work in production. We've built LLM-powered applications, RAG pipelines, and intelligent automation systems that run reliably at scale — not just in demos. Our AI engineers understand both the machine learning and the product engineering sides.

We don't just integrate an API call to OpenAI. We design the full AI system: data ingestion, chunking strategy, vector storage, retrieval logic, prompt engineering, evaluation framework, and human-in-the-loop workflows where needed.

Who It's For

Perfect for these situations

Document Intelligence

Extract structured data from unstructured documents at scale.

AI Product Features

Embed LLM-powered capabilities directly in your product workflow.

Intelligent Automation

Replace manual processes with AI agents that work 24/7.

How We Work

Our process, step by step

Data & Use-Case Audit

3–5 days

We assess your data quality, volume, and structure — and define exactly what the AI system needs to do, and how success will be measured.

Pipeline Architecture

1 week

Design the full AI system: ingestion, chunking, embeddings, retrieval, and the LLM integration layer. No black boxes.

Build & Evaluate

2–6 weeks

Build the pipeline, tune prompts and retrieval parameters, and run rigorous accuracy evaluation against a ground-truth dataset.

Productionise & Monitor

1–2 weeks

Deploy with latency monitoring, drift detection, and a human-in-the-loop review interface. AI in production requires ongoing oversight.

Timeline

What to expect, week by week

Week 1

Data Audit & Design

Assess data, define success metrics

Weeks 2–4

Pipeline Build

Ingestion, embeddings, retrieval, LLM layer

Week 5

Evaluation & Tuning

Accuracy benchmarking, prompt optimisation

Week 6

Production Deploy

Monitoring, drift detection, HITL interface

Pricing Hint

Investment range

$15,000 – $80,000

Typical range · 4–12 weeks

Complexity-driven. A focused RAG integration starts at the lower end; multi-agent systems with custom fine-tuning and eval infrastructure scale significantly higher.

Every project is scoped individually. Book a free call to get a precise estimate.

Deliverables

What you'll receive

AI system architecture design

LLM integration and prompt library

Vector database setup and indexing pipeline

Evaluation framework and accuracy benchmarks

Monitoring and drift detection setup

Human-in-the-loop review interface

Case Study

See this service in action

AI / ML·Nexloom

The Challenge

Nexloom's team was spending 40+ hours a week manually extracting structured data from unstructured documents. Their previous AI vendor achieved only 62% accuracy — not good enough for a production system. They needed 90%+.

Our Solution

We built a custom RAG pipeline with domain-tuned chunking, Pinecone vector search, and a GPT-4 extraction layer with a human-in-the-loop review interface that fed corrections back as fine-tuning data. Accuracy hit 97% in 6 weeks.

Results

97%

Extraction Accuracy

80%

Less Manual Work

50%

Fewer Support Tickets

6 weeks

To Production

“Their AI integration expertise is genuinely rare. They shipped features our previous agency said were technically impossible.”
— Priya Nair, CTO, Nexloom

“We'd been told by two other agencies that what we needed wasn't possible with current LLM technology. Fyutrex not only proved them wrong — they shipped it in 6 weeks with 97% accuracy. That changed everything for our business.”

Priya Nair

CTO, Nexloom

Why Choose Us

What makes Fyutrex different

We evaluate accuracy before deployment — not after

Full stack AI: from data ingestion to the user-facing feature

Every AI system includes monitoring, drift detection, and fallback logic

We've shipped real AI products — not just RAG demos

Model-agnostic: we use the best tool for your use case

Human-in-the-loop design prevents silent failures in production

See It In Action

Related case studies

Nexloom · AI / ML

AI features competitors called impossible

Integrated a retrieval-augmented generation pipeline that reduced their manual data entry by 80% and cut support tickets in half.

80% less manual work · 50% fewer tickets

Read case study

FAQ

Common questions answered

Can't find what you're looking for? Get in touch and we'll answer any question directly.

Do you only work with OpenAI models?

No. We work with OpenAI, Anthropic (Claude), Mistral, Llama, and open-source models. We'll recommend the best model for your use case — considering accuracy, latency, cost, and data privacy requirements.

How do you measure if the AI is actually working?

We build an evaluation framework with a ground-truth dataset before writing a line of AI code. Every pipeline has a measurable accuracy target. You'll see benchmark scores — not just a demo.

Is our data secure when using LLMs?

Yes. We architect AI systems with data security first: no training on your data by default, private deployments when required, and full audit trails of every AI decision.

What if the AI makes mistakes in production?

All our AI systems include human-in-the-loop review for high-stakes decisions, monitoring dashboards, and drift detection. Low-confidence outputs are flagged rather than acted upon.

Can you fine-tune models on our proprietary data?

Yes. We have experience fine-tuning open-source models for domain-specific tasks. We'll advise whether fine-tuning is actually necessary — often prompt engineering and RAG outperform fine-tuning at a fraction of the cost.

Ready to get started with AI Integration?

Let's talk. A free 30-minute call will map out exactly what you need.

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