AI Solutions That Ship to Production, Not Just Demos
Everyone has an AI prototype. Few have AI running reliably in production. We build LLM features, document automation, and intelligent search that survive real data, real users, and real edge cases, with evaluation baked in.
Trusted by teams at
95%
Document-extraction accuracy on a 500-doc benchmark
5x
Faster resume screening inside a production ATS
90%
Answer-quality improvement from a RAG assistant
500+
Support tickets auto-triaged per day for one client
The Gap Between an AI Demo and an AI Product Is Enormous
A ChatGPT wrapper impresses in a meeting and falls apart on real data. Production AI needs validation, fallbacks, evaluation sets, and human oversight: engineering, not prompting.
Demos That Don't Survive Real Data
The prototype handled ten clean examples. Production means malformed documents, adversarial inputs, and edge cases nobody demoed.
No Way to Measure Quality
Without evaluation sets and baselines, you can't tell whether a prompt change made things better or worse. Most AI features ship unmeasured.
Automation Without Oversight
Full automation of judgment calls is how AI projects become liabilities. Production AI needs confidence thresholds, fallbacks, and human review queues.
AI Engineering Across the Full Stack
From LLM features inside your existing product to complete AI platforms built from zero.
LLM Feature Integration
Claude, GPT, and Gemini features inside your existing product (classification, summarization, generation) with validated schemas and fallbacks.
RAG & Semantic Search
Grounded, citation-backed answers over your own content. Vector retrieval, "I don't know" behavior, and automatic re-indexing.
Document AI & OCR Pipelines
Invoices, contracts, resumes, extracted to validated schemas with OCR, LLM parsing, anomaly detection, and human review queues.
AI Workflow Automation
Ticket triage, data categorization, content routing: repetitive judgment work automated with confidence thresholds and escalation rules.
Evaluation & Model Pipelines
Evaluation harnesses, benchmark sets, and multi-model pipelines, so every change to prompts or models is measured, not guessed.
AI Code Rescue
AI-generated codebase collapsing in production? We audit, harden, and stabilize: security, performance, and test coverage.
Production AI, With Numbers Behind It
Here's what AI engineering looks like when it's measured against evaluation sets, not vibes.
AI-Powered CLM Platform: Zero to Production in 5 Months
The Challenge
A legal-tech company needed a full contract lifecycle management product (generation, review, negotiation) capable of genuinely replacing how legal teams work today.
What Solution We Built
- AI contract generation from templates and playbook-preferred language
- Metadata extraction: parties, dates, values, governing law
- Clause-by-clause comparison against playbook positions
- Automatic risk scoring with suggested alternative language
- Playbook builder: preferred positions and approved fallbacks
- Two AI models in the pipeline, each assigned to what it does best
The same discipline across smaller engagements: a RAG assistant that improved answer quality 90% and cut support tickets 40% (measured on an evaluation set), invoice automation hitting 95% extraction accuracy on a 500-document benchmark, and ticket triage handling 500+ tickets a day with human-in-the-loop corrections.
From Use Case to Production AI in Four Steps
Every AI engagement starts with the question most vendors skip: how will we know it's working?
Use-Case Validation & Data Audit
We test your use case against your real data before committing to a build, including telling you if AI is the wrong tool.
A small evaluation set is built in week one; it becomes the quality contract for the project.
Fixed Scope Proposal
Precise proposal within 48 hours: architecture, accuracy targets, timeline, price. Fixed.
Scope includes confidence thresholds, fallbacks, and human review flows. Not just the model call.
Sprint Build & Measured Demos
Weekly sprints demoed against the evaluation set: accuracy numbers every Friday, not cherry-picked examples.
Shadow-mode validation against real workloads before anything goes live.
Launch, Handover & Support
Deployed with monitoring, audit logs, and the evaluation harness handed over. Available 30 days post-launch.
Your team can measure every future prompt or model change the same way we did.
Single AI feature → production in 4–6 weeks. Full AI platform → phased over 3–5 months. You'll know which before we start.
Why Companies Choose BestlaTech for AI Work
Measured, Not Vibes
Every build ships with an evaluation set and accuracy baselines. 95% extraction accuracy means validated against a 500-document benchmark, not a demo that went well.
Human-in-the-Loop by Design
Confidence thresholds, review queues, and zero structural auto-rejection. AI assists; your people keep the judgment calls.
You Talk to the Founder
Shubham leads every engagement. No account managers, no PMs in the middle. Weekly demos and direct Slack access throughout.
Have an AI Use Case? Let's Find Out if It's Real.
30-minute call. By the end, you'll know whether your use case is production-viable, what the architecture looks like, and what a fixed-scope build involves.
Book Your Free Discovery Call (opens in new tab)Fixed scope. Fixed price. Zero surprises.
Frequently asked questions
Questions CTOs and founders ask us most.