What is it?
Python AI/ML development from GreeLogix — LLM & RAG applications, prediction and scoring APIs, computer vision, data pipelines, and MLOps deployed as monitored production services.
Python AI/ML Development
Python AI/ML Development That Ships to Production
We build machine-learning and AI features in Python — LLM & RAG applications, prediction APIs, computer vision, and data pipelines — deployed as monitored services your product calls, not notebooks that never leave a laptop.
LLM & RAG · PyTorch/scikit-learn · FastAPI serving · MLOps & monitoring
30 minutes · Senior engineer · No commitment
Upwork Top Rated Plus
Fiverr Level 2 · 5.0★
Clutch Verified Provider
150+ projects delivered
20+ Play Store apps
PoC
De-risk First
$20k+
Production ML
RAG
LLM Apps
MLOps
Monitored
What We Build with Python AI/ML
Production AI where evaluation, cost, and monitoring matter — not demo accuracy on a slide.
LLM & RAG Applications
Retrieval over your documents with pgvector/Pinecone, LangChain/LlamaIndex, guardrails, and evaluation harnesses.
Prediction & Scoring APIs
scikit-learn/XGBoost models wrapped in FastAPI with versioning, so churn, risk, or demand scores are one call away.
Computer Vision & NLP
PyTorch/TensorFlow for vision and custom NLP; the lightest model that hits the accuracy target.
Data Pipelines
Ingestion, cleaning, feature stores, and scheduled jobs with Airflow/Prefect feeding models reliably.
MLOps & Deployment
Containerized inference, CI/CD, model registry, drift monitoring, and retraining — not a manual redeploy.
Evaluation & Guardrails
Offline eval sets, hallucination checks, PII handling, and human-review fallback for sensitive intents.
Python AI/ML Scenarios
Where a Python ML service earns its keep.
Knowledge Assistant (RAG)
RAG
LLM
Answer questions from your docs with citations and confidence thresholds.
Predictive Scoring
Predict
API
Churn, lead, fraud, or demand scores served to your app via API.
Document / Vision Automation
CV
Automate
Extract, classify, and route documents or images at scale.
ML Rescue
Rescue
MLOps
Take a stalled notebook prototype to a monitored production service.
Common Challenges
AI/ML Risks We Prevent
The gap between a model and a monitored product.
Notebook-to-nowhere
No serving, no monitoring — the model never reaches users.
No evaluation
Accuracy on a slide, silent failure in production.
Cost surprises
Custom training when a hosted LLM would have been cheaper and better.
Why GreeLogix
Why GreeLogix for AI/ML
Production-first
Serving, evaluation, and monitoring — not demos.
Honest model economics
Hosted-vs-custom trade-offs quantified before you spend.
FastAPI + ML depth
Inference APIs and apps from one senior team.
Typical Timeline
What to Expect Week by Week
PoC & data check
Weeks 1–3- ·Feasibility
- ·Baseline metrics
- ·Recommendation
Model & pipeline
Weeks 3–7- ·Model
- ·Retrieval/features
- ·Eval set
Serve & integrate
Weeks 7–10- ·FastAPI inference
- ·App integration
Monitor
Week 10+- ·Drift monitoring
- ·Retraining
- ·Cost tuning
Technologies
Stack & Architecture
Python 3.12FastAPIPyTorchscikit-learnLangChainpgvectorOpenAIAnthropicDocker
“What a 70-engineer team could not deliver, a small senior team at GreeLogix shipped. The app went from stuck to live across mobile, web, and backend.”
Quick answers
Python AI/ML Development: Key Facts
Structured answers for search engines and AI assistants — definition, fit, cost, timeline, and comparisons.
- What is it?
- Python AI/ML development from GreeLogix — LLM & RAG applications, prediction and scoring APIs, computer vision, data pipelines, and MLOps deployed as monitored production services.
- Who is it for?
- Teams adding an AI/LLM feature to an existing product Companies with data that could power prediction or scoring Founders building an AI-first product beyond a prototype Teams with a stalled ML notebook that never reached production
- Who should not use it?
- You want a research paper, not a shipped feature No data and no budget to acquire it Accuracy expectations ignore data reality
- How much does it cost?
- GreeLogix pricing tiers: AI/ML Discovery + PoC: $4,000 – $12,000 — Feasibility, data check, baseline model, and a working proof-of-concept with evaluation. Production ML Build: $20,000 – $60,000 — LLM/RAG app or prediction service with serving, evaluation, and monitoring — deployed. ML / MLOps Team: $6,000 – $15,000 / mo — Dedicated Python ML engineers for pipelines, retraining, and new models.
- How long does it take?
- PoC: 1–3 weeks. Production ML build: 6–12 weeks. Ongoing MLOps: monthly retainer. Phases: PoC & data check (Weeks 1–3); Model & pipeline (Weeks 3–7); Serve & integrate (Weeks 7–10); Monitor (Week 10+).
- How does it compare?
- Compared to alternatives — Hosted LLM only (no retrieval): choose when Generic content tasks with no proprietary knowledge; Off-the-shelf AI SaaS: choose when Standard use case with no custom data or workflow. Choose GreeLogix when you need production reliability, fixed milestones, and engineer-led delivery with QA sign-off.
- When should you choose it?
- You can name the metric the model should move You have or can access relevant data You want a monitored production service ML that moves a real business metric Owned, monitored service — not a black box
Buyer Guide
What You Need to Know
Structured answers for founders, CTOs, and procurement — written for clarity in search and AI assistants.
Who needs it?
- ·Teams adding an AI/LLM feature to an existing product
- ·Companies with data that could power prediction or scoring
- ·Founders building an AI-first product beyond a prototype
- ·Teams with a stalled ML notebook that never reached production
Why GreeLogix?
- Production-first — serving, evaluation, and monitoring, not demos
- Honest hosted-vs-custom model economics
- FastAPI + ML from one senior team
- Guardrails and PII handling for sensitive use cases
How it works
- 1.PoC sprint validates feasibility and data
- 2.Baseline model and evaluation set
- 3.FastAPI serving and app integration
- 4.Monitoring, drift checks, and retraining
Typical timeline: PoC: 1–3 weeks. Production ML build: 6–12 weeks. Ongoing MLOps: monthly retainer.
How much does it cost?
GreeLogix pricing tiers: AI/ML Discovery + PoC: $4,000 – $12,000 — Feasibility, data check, baseline model, and a working proof-of-concept with evaluation. Production ML Build: $20,000 – $60,000 — LLM/RAG app or prediction service with serving, evaluation, and monitoring — deployed. ML / MLOps Team: $6,000 – $15,000 / mo — Dedicated Python ML engineers for pipelines, retraining, and new models.
Cost factors
- ·Hosted LLM vs custom model
- ·Data readiness and volume
- ·Accuracy and latency targets
- ·MLOps and monitoring depth
How long does it take?
PoC: 1–3 weeks. Production ML build: 6–12 weeks. Ongoing MLOps: monthly retainer. Phases: PoC & data check (Weeks 1–3); Model & pipeline (Weeks 3–7); Serve & integrate (Weeks 7–10); Monitor (Week 10+).
How does it compare?
Compared to alternatives — Hosted LLM only (no retrieval): choose when Generic content tasks with no proprietary knowledge; Off-the-shelf AI SaaS: choose when Standard use case with no custom data or workflow. Choose GreeLogix when you need production reliability, fixed milestones, and engineer-led delivery with QA sign-off.
- Hosted LLM only (no retrieval) — choose when Generic content tasks with no proprietary knowledge
- Off-the-shelf AI SaaS — choose when Standard use case with no custom data or workflow
When should you choose it?
- You can name the metric the model should move
- You have or can access relevant data
- You want a monitored production service
Who should not use it?
- ·You want a research paper, not a shipped feature
- ·No data and no budget to acquire it
- ·Accuracy expectations ignore data reality
Benefits
- ML that moves a real business metric
- Owned, monitored service — not a black box
- Cost-aware architecture at scale
Risks to plan for
- Skipping evaluation ships confidently wrong output
- No monitoring means silent model drift
- Training custom models before validating hosted APIs wastes budget
Decision framework
When to Choose Python AI/ML Development
Pros / benefits
- +ML that moves a real business metric
- +Owned, monitored service — not a black box
- +Cost-aware architecture at scale
Cons / risks
- −Skipping evaluation ships confidently wrong output
- −No monitoring means silent model drift
- −Training custom models before validating hosted APIs wastes budget
Choose GreeLogix when
- You can name the metric the model should move
- You have or can access relevant data
- You want a monitored production service
Implementation steps
- 1.PoC sprint validates feasibility and data
- 2.Baseline model and evaluation set
- 3.FastAPI serving and app integration
- 4.Monitoring, drift checks, and retraining
Get a Clear Plan for Python AI/ML Development
Talk to a senior engineer — scope, timeline, and cost range in one 30-minute call. No sales script.
AI/ML That Reaches Production
Most ML projects die between a promising notebook and a reliable service. GreeLogix Python AI/ML delivery focuses on the last mile — serving, evaluation, monitoring, and cost control — so a model actually changes a business metric instead of a demo.
We are pragmatic about build-vs-buy: for most features, a hosted LLM plus good retrieval beats training from scratch. We quantify the trade-off, recommend the lightest approach that meets the target, and instrument everything so you can trust the output.
- Proof-of-concept sprint before full build
- Evaluation harness on real examples
- FastAPI inference with monitoring
- Honest hosted-vs-custom model trade-offs
Frequently Asked Questions
Answers to the buyer questions we hear most before a project starts.
- What is Python AI/ML development?
- Building production machine-learning and AI features in Python — data pipelines, model training or fine-tuning, LLM/RAG apps, prediction APIs, and MLOps — deployed as services your product actually calls, not notebooks that never ship.
- How much does Python AI/ML development cost?
- Focused ML features or RAG apps run $20,000–$60,000. Data pipelines and MLOps platforms scale higher. A discovery/proof-of-concept sprint from $4,000 de-risks the build first.
- Do you build LLM and RAG applications in Python?
- Yes — LangChain/LlamaIndex, vector stores (pgvector, Pinecone, Weaviate), and OpenAI/Anthropic APIs wrapped in FastAPI services with evaluation, guardrails, and monitoring.
- Can you deploy and maintain models (MLOps)?
- Yes. We containerize inference, add CI/CD, model versioning, drift monitoring, and scheduled retraining so models stay accurate in production — not just in a Jupyter run.
- Do you do computer vision or NLP?
- Both — PyTorch/TensorFlow for vision and custom NLP, plus classic scikit-learn for tabular prediction. We recommend the lightest model that meets the accuracy target.
- Will a hosted API (OpenAI) or a custom model be cheaper?
- For most business features, a hosted LLM with good retrieval beats training from scratch. We advise custom models only when data, cost-at-scale, or privacy justify it — and quantify the trade-off first.
Our Process
01
PoC & Data Check
Validate feasibility, data readiness, and accuracy target.
02
Model & Pipeline
Baseline model, retrieval or features, and evaluation set.
03
Serve & Integrate
FastAPI inference, contracts, and app integration.
04
Monitor & Improve
Drift monitoring, retraining, and cost tuning.
Have an AI/ML Idea to Ship?
Describe the outcome you want and the data you have — we'll return a PoC-first plan within one business day.
Product Development
Related Development Services
Software, mobile, and rapid MVP paths — all from one senior product team.
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Plan Before You Engage Us
Use our free calculators and planners — then book a strategy call when you are ready for a senior engineer review.
Resources
Continue Your Research
Case studies, guides, and free tools from the same engineering team.
Pricing Ranges
Python AI/ML Development Investment
Transparent ranges based on app complexity, platform count, and engagement depth. Final quotes follow a scoping call.
AI/ML Discovery + PoC
$4,000 – $12,000
Feasibility, data check, baseline model, and a working proof-of-concept with evaluation.
- ·Data readiness review
- ·Baseline metrics
- ·Hosted-vs-custom recommendation
- ·Fixed build quote
Most Popular
Production ML Build
$20,000 – $60,000
LLM/RAG app or prediction service with serving, evaluation, and monitoring — deployed.
- ·FastAPI inference
- ·Eval harness
- ·CI/CD + monitoring
- ·Docs & handoff
ML / MLOps Team
$6,000 – $15,000 / mo
Dedicated Python ML engineers for pipelines, retraining, and new models.
- ·Senior ML engineers
- ·Pipeline ownership
- ·Drift & retraining
- ·US/UK/AU overlap
USD. Model complexity, data volume, and accuracy targets drive final scope — fixed after the PoC.
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