solution
AI/ML Development Services .
iSkylar builds production-grade AI and ML systems that go beyond experiments, from predictive analytics and NLP to computer vision and generative AI, we engineer intelligent solutions that plug into your existing stack and deliver measurable outcomes from day one.
What We Build
Custom ML Model Development
We design, train, and deploy supervised, unsupervised, and deep learning models built around your specific data and business objective — not generic templates.
Predictive Analytics & Forecasting
We turn your historical data into forward-looking intelligence — demand forecasting, churn prediction, risk scoring, and anomaly detection pipelines built for production.
Natural Language Processing
From intelligent document extraction and semantic search to fine-tuned chatbots and sentiment analysis — we build NLP systems that understand language in your domain.
Computer Vision
Defect detection, document OCR, identity verification, and real-time image classification — we build vision systems that integrate directly into your operations or product.
Generative AI & LLM Integration
We build RAG pipelines, custom GPT wrappers, and LLM-powered workflows using OpenAI, Anthropic, and open-source models — grounded in your data, not hallucinations.
MLOps & Model Maintenance
Deployed models degrade. We set up monitoring, automated retraining pipelines, and CI/CD for ML so your models stay accurate as data drifts over time.
AI-POWEREDLearn how we work
Delivered faster with AI tooling
We don't just write code; we orchestrate intelligence. By integrating Copilot-assisted generation and AI-driven automated testing, we reduce time-to-market by up to 40%.
Copilot-Assisted Generation
LLM-backed development to eliminate boilerplate delays.
AI Predictive Testing
Anticipating edge cases before they enter production.
ENGINEERED FOR PERFORMANCE
The iSkylar Tech Stack
Python
TensorFlow
PyTorch
Scikit-Learn
HuggingFace Transformers
LangChain
OpenAI API
FastAPI
Apache Spark
AWS SageMaker
GCP Vertex AI
Azure ML
Docker
Kubernetes
PostgreSQL
Our Development Journey
A streamlined, transparent path from vision to launch.
01
Discovery & Data Audit
1–2 weeks
We assess your data readiness, map your business problem to the right ML approach, and produce a solution architecture doc with success metrics agreed upfront.
02
Data Engineering
1–3 weeks
Cleaning, feature engineering, pipeline design, and train/test split strategy. We handle messy, incomplete, or multi-source data — no clean dataset required before we start.
03
Model Development
3–8 weeks
Iterative modelling sprints: baseline → tuning → evaluation. You see weekly demos with benchmark comparisons so you always know where accuracy stands against the target.
04
Integration & API Build
1–2 weeks
We package the model into a REST or gRPC API, connect it to your existing systems, and run end-to-end testing across staging and production environments.
05
QA & Performance Testing
1–2 weeks
Stress testing, latency benchmarking, edge-case validation, and bias/fairness audits. Nothing ships until it passes every scenario in your acceptance criteria.
06
Deployment & Handover
1 week
Production rollout on your chosen cloud provider, live monitoring dashboards, full runbook documentation, and team training so your engineers can own it from day one.
Our Proven Track Record
IMPACTMETRICS
MODEL ACCURACY
94%
Document classification model accuracy for a legal-tech client
EFFICIENCY GAIN
70%
Reduction in manual processing time for a logistics company using our AI routing system
PRECISION IMPROVEMENT
60%
Decrease in fraud false-positives for a FinTech platform after replacing rule-based scoring
RELIABILITY
99.9%
Uptime across all production ML inference APIs we manage
Flexible Engagement
Partnership structures tailored to your scale and velocity.
Proof of Concept
Validate your AI idea on real data before committing to a full build. Includes problem scoping, baseline model, evaluation report, and an integration blueprint your team can act on.
- Defined Scope
- Fixed Budget
POPULAR
Production AI System
End-to-end delivery — data engineering, model development, API build, cloud deployment, monitoring setup, and 3 months of post-launch support. Most clients start here.
- Full Integration
- Infinite Scalability
Dedicated AI Team
A dedicated squad of ML engineers, data scientists, and MLOps specialists embedded in your product team on a monthly retainer. Ideal for ongoing AI roadmap execution.
- Defined Scope
- Fixed Budget
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"iSkylar transformed our legacy banking app into a modern, lightning-fast experience. Their AI-driven approach saved us months in engineering time."
JD
James Donovan
CTO, GlobalFin
"
"The level of technical precision and UI craftsmanship is unmatched. They don't just build apps; they build business advantages."
SL
Sarah Lin
Product Head, LuxeRetail
Frequently Asked Questions
What types of AI/ML projects do you take on?+
We work across supervised learning, unsupervised learning, NLP, computer vision, and generative AI — in industries including fintech, healthcare, logistics, e-commerce, and B2B SaaS.
Do we need clean, structured data before we start?+
No. Data preparation is built into our process. We audit your data sources during the discovery phase and handle cleaning, labelling guidance, and pipeline design as part of the engagement.
How long does a typical AI/ML project take?+
A proof of concept runs 4–6 weeks. A full production system is typically 10–18 weeks depending on data complexity, integration scope, and the number of models involved.
Can you integrate AI into our existing product or platform?+
Yes. We build model-serving APIs designed for clean integration into any existing stack — whether that is a SaaS product, internal operations tool, or enterprise system.
Who owns the code and the trained models?+
You do. Full IP transfer — all source code, trained model weights, training pipelines, and documentation — is included at project close, no exceptions.
What happens if the model performance degrades over time?+
We set up monitoring and drift alerting as standard on every deployment. Clients on a retainer get scheduled retraining and drift response handled automatically.
Do you use third-party AI APIs like OpenAI or AWS Bedrock?+
Yes. We frequently combine custom-trained models with third-party APIs to optimise cost, latency, and capability — and we advise on the right mix for your use case and data privacy requirements.
Ready to build your breakthrough?
Contact us today for a technical consultation and a detailed project estimate.
Get a free estimate