You will get a reliable AI feature: I diagnose and fix unstable LLM pipelines
Rising Talent
Project details
Your AI feature demos brilliantly and embarrasses you in production. Outputs that change shape between runs, JSON that will not parse, timeouts under load, a model bill that climbs while quality does not. You are not imagining it, and it is fixable.
I build multi-model AI pipelines that run unattended in three live SaaS products, processing real customer work daily across Claude, OpenAI and Gemini. The uncomfortable truth I have learned shipping them: most unreliable AI features are not model problems, they are missing engineering around the model — no structured output enforcement, no retries, no fallbacks, no evals.
What I diagnose and fix:
Inconsistent or wrong outputs and broken JSON
Prompt structure, context handling and token waste
Retry, timeout and fallback behaviour
Runaway model costs
No way to test whether a prompt change made things better or worse
You receive a written diagnosis ranked by impact. Higher tiers include rebuilding the worst failure point and full hardening: provider fallbacks, output validation and an eval harness so reliability becomes measurable.
Direct API, Vercel AI SDK and LangChain codebases all fine.
I build multi-model AI pipelines that run unattended in three live SaaS products, processing real customer work daily across Claude, OpenAI and Gemini. The uncomfortable truth I have learned shipping them: most unreliable AI features are not model problems, they are missing engineering around the model — no structured output enforcement, no retries, no fallbacks, no evals.
What I diagnose and fix:
Inconsistent or wrong outputs and broken JSON
Prompt structure, context handling and token waste
Retry, timeout and fallback behaviour
Runaway model costs
No way to test whether a prompt change made things better or worse
You receive a written diagnosis ranked by impact. Higher tiers include rebuilding the worst failure point and full hardening: provider fallbacks, output validation and an eval harness so reliability becomes measurable.
Direct API, Vercel AI SDK and LangChain codebases all fine.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, AI Content Creation, AI Mobile App Development, AI-Generated Code, AI-Generated Video, Sentiment Analysis, Text RecognitionAI Development Language
PythonAI Models
ChatGPT, GPT-4, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$395
|
Standard
$895
|
Advanced
$1,950
|
|---|---|---|---|
| Delivery Time | 4 days | 10 days | 21 days |
Number of Revisions | 1 | 1 | 2 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | |||
Model Documentation | - | - | - |
Model Monitoring | |||
Model Testing & Optimization | |||
Model Tuning | |||
Natural Language Processing | - | - | - |
NLP Tokenization | |||
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | - | - |
Source Code | - | - | - |
Frequently asked questions
About Justin
Senior SaaS Developer - Next.js, TypeScript, Supabase, Stripe, AI
Burton Joyce, United Kingdom - 2:34 pm local time
Most recently I built SyncStudio from zero to production — product strategy, full-stack development (Next.js, Supabase, Stripe, Inngest), and a 64-page SEO content architecture driving organic acquisition. Before that, 10 years as COO/CTO of AppInstitute, a globally deployed SaaS platform.
I take on work across the full stack:
Product — PRDs, roadmaps, pricing models, feature prioritisation. Commercially minded, not just technically.
Development — Next.js, React, Node.js, Supabase, API integrations, AI workflows. Production-grade, not prototype-grade.
I build SaaS products end-to-end and fix the ones that are quietly costing you money. Three live products of my own — built from zero on Next.js, TypeScript, Supabase, Stripe and Inngest, with multi-model AI pipelines running unattended daily. Before that, ten years as CTO/COO of AppInstitute, a SaaS platform I co-founded, scaled to £7.5m in revenue and exited.
Building from scratch: I take products from idea to production — PRD, architecture, full-stack build, billing, AI workflows, launch. Most recently SyncStudio, zero to production end-to-end, including a 64-page SEO content architecture driving organic acquisition. Because I have built for myself with my own money at stake, you get the commercial decisions alongside the code: what to build, what to skip, what it should cost.
Fixing what exists: Stripe billing that double-charges or drifts out of sync. Supabase databases where one missing RLS policy exposes customer data while everything looks fine. AI features that demo brilliantly and fail in production. Fixed-price audits in my project catalog are the fastest way to start here — plain-English reports ranked by business risk, actionable by any developer, not just me.
Either way, everything is production-grade, not prototype-grade: idempotent payment handling, tested security policies, AI pipelines with retries, fallbacks and evals. Read-only access is enough to start on audits, and I sign NDAs without fuss.
Message me with what you are building, or what is going wrong.
Steps for completing your project
After purchasing the project, send requirements so Justin can start the project.
Delivery time starts when Justin receives requirements from you.
Justin works on your project following the steps below.
Revisions may occur after the delivery date.
Reproduce the failure
I run your pipeline against real and failing inputs until I can trigger the problem on demand. An AI bug you cannot reproduce is an AI bug you cannot fix, so this comes before any opinions about prompts or models.
Trace the pipeline end to end
I review every stage: prompt construction, context and token budgets, model and parameter choices, output parsing, retries and timeouts, and error handling. Most "AI is unreliable" problems are engineering problems sitting next to the model.