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AI & Automation Services

Transform Your Business with Production-Grade AI Systems

At Naqvix, we leverage cutting-edge AI technologies to build custom solutions that drive real efficiency gains and measurable outcomes. Our AI engineers build chatbots, lead scoring systems, document processors and intelligent automation — deployed in production, running 24/7, integrated with your existing workflows.

AI Solutions We Deliver

End-to-end AI and automation services, from strategy to deployment and beyond.

Custom AI Chatbots

Trained on your data. Handles FAQs, qualifies leads, books appointments and supports customers 24/7 without human intervention. Deployed on website, WhatsApp or your platform.

AI Lead Scoring

Score every lead 0-100 in real time based on behaviour and engagement. Proactive suggestions tell your team exactly who to contact and when — before opportunities go cold.

Process Automation

Eliminate manual data entry, document processing, email routing and repetitive workflows. We identify your highest-cost manual processes and automate them properly.

Document AI & NLP

Extract, classify and process information from contracts, invoices, medical records and emails automatically. Eliminates manual extraction at any scale.

Predictive Analytics

ML models that forecast demand, flag churn risk and surface hidden insights in your historical business data. Decisions driven by data, not instinct.

AI Strategy Consulting

Not sure where AI fits in your business? We audit your processes, identify the highest-ROI automation opportunities and build a practical AI implementation roadmap.

AI for Every Industry

We have deployed AI solutions across these industries and understand the data, compliance and workflow requirements unique to each.

Healthcare

AI-powered diagnostic support, patient intake automation, billing query handling and appointment scheduling. Built with HIPAA compliance and healthcare workflow integration.

Patient intake AI
Billing query chatbot
Appointment automation
HIPAA compliant

Real Estate

AI lead scoring platforms that rank buyers and sellers 0-100, predict closing likelihood and tell agents exactly who to call next based on real behaviour signals.

Lead scoring 0-100
Predictive pipeline
Agent task automation
CRM integration

E-Commerce

AI-powered product recommendations, customer support chatbots, inventory forecasting and personalised email automation that increase revenue per customer.

Product recommendations
Support chatbot
Inventory forecasting
Email personalisation

Legal Services

Document review automation, contract analysis, legal research assistance and client intake chatbots that reduce administrative time for legal professionals.

Document review AI
Contract analysis
Client intake bot
Research automation

Financial Services

Fraud detection models, credit risk scoring, automated financial reporting and customer service chatbots for financial institutions and fintech companies.

Fraud detection
Risk scoring
Report automation
Customer chatbot

Logistics

Route optimisation models, demand forecasting, dispatch automation and predictive maintenance systems for logistics and transportation businesses.

Route optimisation
Demand forecasting
Dispatch automation
Maintenance prediction

Education

Personalised learning recommendations, automated essay feedback, student performance prediction and administrative task automation for educational institutions.

Learning personalisation
Essay feedback AI
Performance prediction
Admin automation

Customer Support

AI chatbots that handle 60-70% of support queries without human intervention, route complex cases with full context and learn from every conversation.

Query automation
Intelligent routing
Sentiment analysis
Continuous learning

How We Build AI Solutions

A proven nine-step process from problem identification to production deployment.

01

Problem Identification

We start by understanding your business challenges, manual processes and objectives. We identify specifically which problems AI can solve with measurable ROI.

02

Competitive & Market Analysis

We analyse how competitors and industry leaders are using AI and identify opportunities for your business to gain a meaningful edge.

03

Data Assessment

We evaluate your existing data sources, quality and accessibility. Good AI requires good data — we identify what you have and what may be needed.

04

Model & Approach Selection

Based on your specific problem and data, we select the most appropriate AI approach — whether a fine-tuned foundation model, a custom ML model or a RAG system.

05

Proof of Concept

Before full development, we build a PoC to validate feasibility and demonstrate value. You see it working before we commit to full-scale build.

06

Model Development & Training

We build, train and fine-tune the AI system using your data. Iterative refinement until performance meets the agreed benchmarks.

07

Testing & Evaluation

Rigorous testing against predefined metrics. Edge case testing, bias evaluation and performance benchmarking before any production deployment.

08

System Integration

We integrate the AI system into your existing platforms — your CRM, your website, your internal tools — so it works within your actual workflow.

09

Deployment & Monitoring

Production deployment with continuous monitoring. We track performance, retrain as needed and ensure the system improves with real-world usage.

Why Businesses Trust Naqvix for AI

Production-grade AI, full data ownership and measurable ROI from day one.

Production-Ready AI

We do not build proofs of concept that never ship. Every AI system we deploy runs reliably in production, handles edge cases and improves over time with real usage.

Your Data Stays Yours

We build on your infrastructure. No third-party data sharing, no vendor lock-in, no data used to train other models. Your data stays completely under your control.

Measurable ROI

Clear success metrics agreed before we start. Time saved, leads qualified, costs reduced — all tracked and reported in plain English every week.

End-to-End Ownership

We own the entire process — from strategy and model selection through to integration, deployment and ongoing monitoring. One team, full accountability.

0/7AI System Operation
0%+Query Automation Rate
0%Client Retention
0xFaster Than Manual

AI Technology Stack

Foundation Models
OpenAI GPT-4oOOpenAI GPT-4o
Google GeminiGGoogle Gemini
Anthropic ClaudeAAnthropic Claude
Hugging FaceHHugging Face
Meta LlamaMMeta Llama
AI Frameworks
LLangChain
LLlamaIndex
TensorFlowTTensorFlow
PyTorchPPyTorch
PythonPPython
Vector & Data
PPinecone
SSupabase pgvector
PostgreSQLPPostgreSQL
MongoDBMMongoDB
Automation & Integration
nn8n
ZZapier
MMake
AWSAAWS
VVercel AI SDK

Trusted by Businesses Across the USA

What Our Clients Say

Naqvix became the engine behind Roadsider. They built everything and run everything. We focus on the product, they handle the rest.

Roadsider Team

Roadsider.com

SaaS + BPO Client

Frequently Asked Questions

Common questions about our AI and automation services.

Ready to Automate?

Book a free AI consultation and let us identify the highest-impact automation opportunities in your business. No obligation, no hard sell — just practical advice from engineers who build AI systems every day.

Book Free AI Audit

How an AI project actually works

Most failed AI projects were never model problems. They started from a technology rather than a decision, or shipped without any way to tell whether the output was good. This is the sequence that avoids both.

  1. Qualifying the use case

    We start from a decision or task that is repetitive, judgement-light and measurable, and check whether it genuinely needs a model at all. A meaningful share of requested AI features are better and more cheaply solved with rules, search or a fixed workflow.

  2. Auditing data readiness

    The available data is assessed for volume, labelling, bias, freshness and permission to use. This is where most timelines are actually decided, because a model cannot compensate for data that does not describe the problem.

  3. Establishing a baseline

    A deliberately simple approach is measured first so there is something to beat. Without a baseline, any model looks impressive and nobody can tell whether the complexity is earning its cost.

  4. Building the evaluation harness

    A fixed evaluation set of real examples with known good answers is assembled before tuning begins, along with the metrics that matter for this task. Without it, prompt and model changes are guesswork dressed up as iteration.

  5. Grounding, integration and human review

    Outputs are grounded in your own retrieved content rather than the model’s memory, and routed through a human checkpoint wherever a wrong answer carries real cost. The interface matters as much as the model — people ignore assistance they cannot verify.

  6. Monitoring drift, quality and cost

    Live inputs, output quality and spend are tracked continuously after launch. Inputs shift, providers change models beneath you, and token costs scale with success, so all three need watching rather than assuming.

What usually goes wrong

The failure patterns that show up repeatedly in AI initiatives.

AI terms, in plain English

The vocabulary that surrounds AI work, defined without mystique.

Large language model
A model trained on very large text collections to predict likely continuations. It produces plausible language rather than verified fact, which is precisely why grounding and evaluation matter.
RAG (retrieval-augmented generation)
Retrieving your own relevant documents and supplying them to the model as context, so answers are based on your content rather than the model’s training memory.
Fine-tuning vs prompting
Prompting instructs an existing model at request time. Fine-tuning trains it further on your examples. Prompting with retrieval solves most business problems more cheaply and stays far easier to change.
Embedding
A numerical representation of meaning that allows text to be compared by similarity rather than exact keywords. It is the mechanism behind semantic search and retrieval.
Hallucination
A confident, fluent, incorrect output. It is a normal property of how these models generate text, not a defect to be patched, which is why systems are designed to constrain and verify rather than to trust.
Evaluation set
A held-out collection of real inputs with known good outputs, used to measure whether a change genuinely improved quality instead of merely feeling better.
Drift
The gradual divergence between the data a system was built for and the data it now receives, causing quality to decay quietly unless it is monitored.
Human in the loop
A deliberate review checkpoint where a person approves or corrects output before it takes effect, used wherever an incorrect result carries real cost.

Related work

Projects we've delivered in this space.

View all work
On-Demand "Uber for Towing" Mobile App Ecosystem — Naqvix case study
BuildAutomotive & Roadside Assistance

On-Demand "Uber for Towing" Mobile App Ecosystem

Towsider

Architecting AtomLead: A High-Conversion SaaS Platform for AI-Powered Lead Automation — Naqvix case study
BuildB2B SaaS / Marketing Technology

Architecting AtomLead: A High-Conversion SaaS Platform for AI-Powered Lead Automation

Atom Leads

Revolutionsing Roadsider: From Strategic Rebranding to AI-Powered Sales Acceleration with Naqvix — Naqvix case study
BuildAutomotive B2B / Roadside Assistance Technology

Revolutionsing Roadsider: From Strategic Rebranding to AI-Powered Sales Acceleration with Naqvix

Roadsider

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