AI Features That Work in Production. Not Just in Demos.
We build LLM integrations and ML pipelines that hold up under real load, with evaluation frameworks, cost controls, and fallback handling engineered in from day one.
- Working proof-of-concept delivered in the first two weeks. Validate before you commit.
- Every LLM integration includes evaluation frameworks, cost controls, and fallback handling.
- All models, pipelines, and IP transferred to you at completion. No vendor lock-in.
Clients include
5.0on Clutch · CEO, Exar North
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The Challenges Most Teams Face. And How We Solve Them.
We have seen these problems across hundreds of engagements. Here is where teams consistently get stuck.
You Know You Need AI But Don't Know Where to Start
Every competitor is adding AI features. You know your product needs it, but you are not sure which use case to prioritise, which model fits your data, or whether the ROI justifies the engineering cost.
LLM Outputs Are Inconsistent and Unreliable
You have tried LLM integrations but the outputs are unpredictable. Users are getting wrong answers, hallucinated data, or responses that do not match your product context. You cannot ship something you cannot trust.
AI API Costs Are Spiralling Out of Control
OpenAI and Anthropic costs are hard to predict and can spike dramatically with scale. Without proper caching, prompt compression, and model routing, your AI feature costs more to run than it generates in value.
Existing AI Features Have Low Adoption
You shipped an AI feature but usage is disappointing. Latency is too high, responses are not relevant enough, or the user experience around the AI is frustrating rather than helpful.
Your Engineering Team Lacks ML Expertise
Your developers are strong on web and backend engineering but have no experience with embeddings, vector databases, fine-tuning, or LLM evaluation frameworks. Hiring ML engineers is expensive and slow.
Uncertainty Between Build and Buy
You are not sure whether to use an off-the-shelf AI tool or build something custom. The wrong decision costs months of rework and significant budget once you hit the limits of a packaged solution.
Everything Included. No Hidden Extras.
One engagement, full-stack execution. We own the outcome, not just the deliverables.
LLM Integration
GPT-4, Claude, Gemini, Llama: we build production-grade LLM features with proper caching, rate limiting, and fallback strategies so your AI works reliably at scale.
RAG and Knowledge Bases
Retrieval-Augmented Generation systems that let your AI answer questions accurately from your own documents, databases, and proprietary data.
Custom ML Models
Classification, recommendation, anomaly detection, and forecasting models trained on your data and optimised for your specific use case.
AI Product Strategy
We help you identify which AI use cases will move the needle for your business before writing a line of code. Most teams start with the wrong problem.
Four Phases. Four Weeks.
Every Checkpoint is Working Software.
No status-report theatre. No slide decks. At every phase you receive something you can read, test, or deploy.
AI Opportunity Assessment
We map your product to AI use cases ranked by business ROI, not technical novelty. You receive a prioritised roadmap with estimated impact per initiative and a data readiness assessment. A working document, not a buzzword deck.
- AI use case inventory
- ROI-ranked roadmap
- Build vs. buy analysis
- Data readiness assessment
Proof of Concept
A working prototype that validates the core AI approach before you commit to a full build. We measure accuracy, latency, and cost at the PoC stage. If the approach does not work, we tell you and help you find one that does.
- Working PoC
- Accuracy and latency benchmarks
- Cost projection
- Go/no-go recommendation
Production Build
Full engineering with evaluation frameworks, monitoring dashboards, rate limiting, and cost controls built in from the start. Weekly demos show real AI output against real data, not mocked responses.
- Production AI feature
- Evaluation framework
- Monitoring and alerting
- Cost controls and budget alerts
Monitoring and Iteration
AI systems degrade without attention. We configure evaluation pipelines, track model performance over time, and help you iterate based on real usage data rather than assumptions.
- Eval pipeline
- Performance dashboard
- Iteration recommendations
- Optional retainer support
Ready to start Phase 1?
Free scoping session. Written proposal within 24 hours.
Enterprise-Grade Tools.
Battle-Tested in Production.
Every technology below has been deployed in production across real client engagements. We choose for longevity and performance, not hype.
Stack selection is driven by project requirements. We advise against over-engineering.
Specific Commitments. Not Marketing Language.
Every firm claims to be reliable, fast, and senior. Here is what those words actually mean in practice when you engage with us.
Business ROI First. Technology Second.
We run an AI opportunity assessment before writing a line of code. Use cases are ranked by business impact, not technical novelty. If a simpler tool solves your problem, we will tell you.
Proof of Concept in 2 Weeks.
We validate the core AI approach with a working prototype before committing to a full build. Accuracy, latency, and cost are measured at the PoC stage. If the approach does not work, we tell you early.
Senior ML Engineers. No Generalists.
Our team has hands-on production experience with LLM integrations, RAG pipelines, vector databases, and custom ML models, not just prompt engineering tutorials.
Cost Controls Built In from Day One.
Semantic caching, prompt compression, model routing, and budget alerts are engineered into every integration. We design AI systems that are cost-predictable at scale, not just in demos.
Evaluation Frameworks, Not Vibes.
We do not ship AI features without a quality measurement framework. Accuracy, latency, and output quality are tracked continuously so you can trust what you have built and catch regressions early.
Your Data Stays Yours.
All models, pipelines, vector indices, and code are transferred to you at completion. We design architectures that work with your existing data stack without requiring you to replace infrastructure.
Built for Your Industry
We bring domain context to every project. Our team has delivered across 10 industry verticals.
“They performed beyond expectations. Clear communication, strong technical understanding. They grasped requirements without needing things repeated.”
“We had been trying to get our AI feature right for months. EnlightLab came in, diagnosed the real problem in a week, and shipped a version that actually worked.”
Testimonials verified via Clutch.co and direct client engagements
Client Outcomes That Speak for Themselves
Real engagements. Real timelines. Real results.
Emblazer.ai
Build an AI agent platform from scratch that lets users delegate research tasks (business directory searches, product research, clinical data) to AI workers and receive structured results.
End-to-end platform built on React, Node.js, and Python on AWS. Full LLM integration, ML pipeline, cloud infrastructure with automated provisioning, and multi-tier subscription billing.
Read full case study“Enlight Lab brought hands-on involvement in addressing platform complexities and delivering working solutions, not just deliverables.”
Huma
A global healthtech company deployed across 4,500+ hospitals needed clinical AI pipelines, remote monitoring dashboards, and automated documentation tooling built to production standards.
AI-powered clinical insight pipelines, virtual ward workflow tools, automated scribing and billing code generation. All integrated into Huma's existing platform with zero downtime.
Read full case study“This integration has significantly enhanced our clinical workflows and improved the quality of patient care outcomes across our deployments.”
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Discovery Call
30 minutes. We review your concept and send a fixed-price proposal within 24 hours of the call.
Book a Free Discovery Call
Tell us about your project and we'll prepare a tailored scope and fixed-price proposal.
Frequently Asked Questions
Everything you need to know before booking a call.
Do you build with OpenAI, Anthropic, or open-source models?
How do you handle hallucinations and accuracy?
What if we don't have training data?
How much will LLM API usage cost in production?
Can you integrate with our existing product and data?
How long does an AI integration take?
How do I get started?
Still have questions?
Ready to Start Your Project?
30 minutes. No pitch deck. We review your concept, define the scope, and send a fixed-price proposal within 24 hours of the call.