01
Lew Innovation
Senior Software Engineer
Automated nurse ticketing and patient enrolment on a healthcare services platform.
Manual clinical admin turned into an automated, auditable workflow.
- React
- Next.js
- TypeScript
- AWS Lambda
- SQS
- API Gateway
Services / AI-powered automation
Use AI where rules run out, with guardrails around every action.
Custom AI systems that read, classify, retrieve and draft inside real business workflows—designed with evaluation, observability and human approval where the stakes require it.
- Starting from
- From $3,500 USD
- Typical timeline
- 3–10+ weeks
- Engagement
- Prototype then production phase
- Delivery
- Remote · Worldwide
A model response is not yet a business system.
Production AI needs a defined job, trusted context, measurable quality and a safe response when confidence is low. I combine model capabilities with ordinary software engineering—permissions, data validation, queues, logs and review screens—so the result can participate in a workflow without pretending uncertainty does not exist.
Best for — Teams processing unstructured documents, requests or knowledge
This is usually a fit when
- 01Staff repeatedly read and extract the same fields from emails, forms or documents.
- 02Support requests need classification, routing or a useful first draft before human review.
- 03Teams cannot find reliable answers across a large collection of internal documentation.
- 04A prototype works in a chat window but has no safe connection to the systems where work happens.
- 05You need evidence that an AI workflow is accurate enough before it reaches customers or production data.
[01]Deliverables
What the engagement can include.
The final scope uses only what the problem needs. These are the common building blocks, not a bundle designed to make a quote larger.
- 01
Document processing
Extract, classify and validate information from PDFs, emails and forms, with review queues for uncertain results.
- 02
Support triage
Categorise requests, retrieve relevant context, route work and prepare responses for a person to approve.
- 03
Knowledge assistants
Search and answer over approved internal material with citations, access controls and clear limits on what is known.
- 04
Action-taking workflows
Connect model decisions to APIs and tools through constrained actions, validation and explicit approval boundaries.
- 05
Evaluation and guardrails
Representative test sets, quality thresholds, fallback behaviour and checks for the failure modes that matter to the task.
- 06
Production operations
Cost tracking, model/version visibility, logs, rate handling and monitoring around the complete workflow.
[02]Approach
How the work moves from idea to production.
Short feedback loops keep the scope honest and surface the difficult decisions while they are still inexpensive to change.
- 01
Choose a bounded decision
We start with one task where inputs, acceptable outputs and business value can be described clearly.
- 02
Build an evaluation set
Realistic examples and difficult edge cases create a baseline for comparing approaches before production use.
- 03
Prototype inside the workflow
The model is tested with the actual context, permissions and human checkpoints it will need to operate safely.
- 04
Release behind controls
Confidence thresholds, review paths, monitoring and rollback keep the first production phase deliberately contained.
Typical stack
- TypeScript
- Python
- OpenAI
- Anthropic
- Vector search
- PostgreSQL
- AWS
- Human review
[03]Relevant experience
Production work behind the offer.
These are shipped systems from my engineering history—not demo projects created to fill a services page.
- 02
ALC Technologies Inc
Senior Backend Developer
Led backend for an English learning platform with an active user base in Japan.
- NestJS
- TypeScript
- Python
- Django
- AWS ECS
- RDS
[04]Questions
Common questions about ai automation.
- 01Do we need to train our own AI model?
- Usually not. Most business workflows are better served by a capable hosted model combined with good context, tools, evaluation and access controls. Fine-tuning is considered only when the evidence shows it solves a specific gap.
- 02Can sensitive information be kept out of model training?
- The design can use providers and account settings that do not train on submitted business data, with data minimisation and access controls around what is sent. Exact requirements are confirmed during scoping.
- 03How do you prevent incorrect AI output from causing problems?
- By limiting the task, grounding responses in approved sources, validating structured output, measuring performance against test cases and requiring human approval for consequential actions.
Next step
Have a ai automation problem?
Bring the current process, the constraint and the outcome you need. You will leave the first call knowing whether it is worth building and what the likely next step looks like.
Replies within 1 business day