AI Development Company | Machine Learning & LLM Integration Services

AI & Machine Learning

AI development company shipping production AI, not demos - LLM integrations, RAG pipelines, AI chatbots, predictive analytics, recommendation engines, and custom machine learning models embedded in real products like Angivore.

From $20k

Starting Price

8–20 Weeks

Avg Timeline

Models + APIs + Dashboards

Deliverables

30+ Projects

Delivered

01 - What's Included

End-to-end AI solutions, from data to deployment

We cover the full AI lifecycle - data strategy, model development, integration, monitoring, and iteration - so your AI investment actually delivers measurable results.

AI Strategy & Scoping

We assess your data, use cases, and business goals to identify the highest-value AI opportunities and define a realistic, ROI-focused roadmap.

Use case identification & prioritization
Data readiness assessment
Build vs. buy vs. fine-tune analysis
ROI estimation & success metrics

Data Engineering & Preparation

Clean, structured, and pipeline-ready data is the foundation of every successful AI project. We build robust data infrastructure.

Data collection & ingestion pipelines
Data cleaning & transformation
Feature engineering & selection
Vector database setup (Pinecone, Weaviate)

Model Development & Training

Custom model development - from classical ML to deep learning and fine-tuned LLMs - trained and validated on your data.

Classical ML (XGBoost, Random Forest, etc.)
Deep learning (CNNs, Transformers, LSTMs)
LLM fine-tuning (GPT, Llama, Mistral)
RAG (Retrieval-Augmented Generation) pipelines

AI Integration & APIs

Seamlessly connect AI capabilities into your existing products and workflows via clean, well-documented APIs.

REST / GraphQL AI API development
LangChain / LlamaIndex orchestration
OpenAI / Anthropic / Hugging Face integration
Streaming responses & real-time inference

UI & Dashboard Development

User-facing AI interfaces - chatbots, analytics dashboards, data visualization, and AI-powered product features.

AI chat interfaces & copilots
Predictive analytics dashboards
Computer vision UI (image/video analysis)
Explainability & confidence score displays

MLOps, Monitoring & Iteration

Production AI requires ongoing care. We set up monitoring, retraining pipelines, and performance tracking so models stay accurate.

Model deployment (Docker, Kubernetes, Sagemaker)
Drift detection & retraining pipelines
A/B testing for model versions
Cost optimization & inference scaling

02 - How We Work

Research-driven AI development with clear business outcomes

Every AI project follows a disciplined discovery-to-deployment process - with frequent demos, measurable milestones, and no black-box methodology.

Discovery & Data Audit

Understand your data landscape, business problem, success criteria, and define the AI approach together.

Weeks 1–2

Data Engineering

Build data pipelines, clean and transform datasets, and establish feature stores for model training.

Weeks 3–5

Model R&D

Rapid experimentation, model selection, training, and evaluation - with regular demos of results.

Weeks 6–10

Integration & UI

Connect models to your product via APIs and build any required frontend interfaces or dashboards.

Weeks 11–15

Testing & Validation

Comprehensive accuracy, bias, and performance testing - plus user acceptance testing of AI features.

Weeks 16–18

Deployment & MLOps

Production deployment, monitoring dashboards, retraining schedules, and knowledge transfer.

Week 19+

03 - AI Tech Stack

Core Language

Python

Deep Learning

TensorFlow

Deep Learning

PyTorch

Machine Learning

scikit-learn

Scientific Computing

NumPy

Data Analysis

pandas

LLM Orchestration

LangChain

Transformers

Hugging Face

04 - Success Stories

Real problems, shipped solutions

AI shipped inside real products with real users - not proof-of-concept demos.

Angivore

Health & Wellness · iOS + Android

The Challenge

Existing wellness apps felt clinical and overwhelming - users had no easy way to log moods, journal, and actually see patterns in their emotional health over time.

Our Consulting Approach

We shipped an AI-powered wellness companion: daily mood check-ins, audio journaling, an AI chatbot that turns entries into personal insights, and gamification that keeps users consistent.

The Result

2 platforms live - App Store and Google Play
AI insights generated from real user journaling data
~8 months from concept to dual-store launch

Growth

Fitness & AI Coaching

The Challenge

Fitness apps handed everyone the same generic plan - no personalization for goals or body type, manual calorie counting, and no sense of real progress.

Our Consulting Approach

We built AI-powered body analysis and smart onboarding that generate personalized training and nutrition plans, paired with trainer coaching subscriptions.

The Result

4.7★ app rating in production
68% 30-day user retention
42% of users convert to trainer subscriptions

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FAQs

AI & Machine Learning FAQs

No. Many high-value AI features are built by integrating foundation models (OpenAI, Anthropic, open-source) with your product data via RAG. If your use case needs a custom or fine-tuned model, we'll tell you - and only when the ROI justifies it.

AI feature integrations start around $20k. Cost scales with data complexity, evaluation requirements, and infrastructure. We always start with a scoping phase that identifies the highest-ROI use case before committing budget.

OpenAI and Anthropic APIs, open-source LLMs, LangChain-style orchestration, vector databases for RAG, plus classic ML for prediction and recommendation. We choose the stack per use case, never the other way around.

Your data stays yours. We design for data isolation, apply role-based access, avoid training on your proprietary data without consent, and can deploy fully private model endpoints when compliance demands it.

Ready to build with us?

Tell us about your project and get a fixed-scope proposal within 48 hours - free consultation, no obligations.