We build AI that survives contact with production.
LLM applications, RAG systems, agents, and automation, built by engineers who've shipped them, not demoed them. We take the parts that break in the real world seriously: latency, cost, evaluation, and the ugly edge cases a demo never sees.
Same vetting bar either way, whether we staff the project or fill the seat. See the rubric
Copilots, chat, and content tools on GPT, Claude, and open models.
Answers grounded in your own documents and data.
Tool-using, multi-step workflows that run real work.
Fine-tuning, evaluation, and the pipelines underneath.
Six things we get asked for most.
Chat interfaces, copilots, and content tools built on GPT, Claude, and open models, with streaming, structured output, and the guardrails that keep them usable once real users arrive.
Retrieval pipelines that ground answers in your own documents, chunking, embeddings, reranking, and evaluation so responses stay accurate as your corpus grows.
Multi-step agents that call tools, hit your APIs, and run real workflows, scoped tightly with retries, human-in-the-loop checkpoints, and observability you can trust.
Fine-tuning and eval harnesses for when prompting isn't enough, dataset curation, training runs, and the offline and online metrics that prove a model actually got better.
Dropping AI into software you already run, search, summarization, classification, and generation added behind clean interfaces, without a rebuild or a risky rewrite.
The unglamorous layer AI actually needs, ingestion, embedding jobs, vector stores, and inference infrastructure that stays cheap and reliable at scale.
Current tools, not last year's.
Shipped, not slideware.
AI support copilot
Cut average ticket resolution time across a high-volume support desk.
Reconciliation agent
Automated recurring reconciliation tasks behind a human-approval gate.
On camera, in their own words.
Why he brought his development work to Code Elevator.
Scoped fast. Shipped on a real timeline.
Building something adjacent?
Answered before you ask.
It depends on scope. A scoped prototype typically runs $6kâ15k; a full production build ranges from $18k upward depending on complexity, data volume, and integration surface. We give you a fixed scope and estimate after the discovery week, no open-ended meters.
Discovery takes a week, a prototype two, and a production build four to eight weeks depending on scope. Most clients have something real in front of users inside a month.
Yes. All work-product IP, code, prompts, fine-tuned weights, and pipelines, assigns to you by contract from day one. Nothing is locked to us or to a proprietary platform you can't leave.
Whatever fits the job, Claude, GPT, and open models like Llama and Mistral. We pick per use case on cost, latency, and quality, and we're not tied to a single vendor. Where open models make sense, we'll self-host them.
Yes. That's usually the point. We build ingestion and embedding pipelines around your documents, databases, and APIs, and we handle the messy parts: cleaning, chunking, access control, and keeping the index fresh.
Yes. We ship to your cloud (AWS, GCP, or self-hosted), wire up tracing and cost dashboards, and set up evaluation that runs continuously so you catch quality regressions before your users do.
We find that out in the evaluation phase, not after launch. If the numbers don't clear the bar we agreed on, we tell you plainly, and sometimes the honest answer is that AI isn't the right tool for that problem yet.
Every way out of this build is already written down.
Code, prompts, models and pipelines: all work-product IP assigns to you by contract from day one, not on final payment.
Nothing is locked to us or to a proprietary platform you can't leave. You get the repository, the documentation, and full access.
Bring us the problem. We'll tell you what's realistic.
No hype, no pilot that goes nowhere. We'll scope it honestly, tell you what's realistic, and ship something that works in production.
We reply within an hour during our working day in India and the UAE.