AI developers who ship past the demo.
Hiring AI developers from CODT Technologies gets you a senior team that has already shipped AI into production — the voice-agent SaaS behind 100K+ live calls and a demand-forecasting platform holding 98% accuracy — with the evals, guardrails and drift monitoring that keep models honest after launch. Not a bench of profiles: engineers, working directly with you.
Scoped like engineering, not magic.
A paid discovery sprint
Every AI engagement starts by asking whether this is actually an ML problem — or whether a heuristic does it cheaper and more reliably. The sprint frames the problem, audits your data and defines what the system must never do, before anyone touches a model.
A fixed, transparent quote
You get a costed, technical plan before any code is written: scope, architecture and an explicit “won’t build” list, priced by outcome rather than by the hour. Cost and timeline are agreed before development starts.
A senior AI team builds eval-first
Baseline first, then justified complexity: every prompt and model change is tested against a frozen evaluation set, with cost guards and quality monitoring from day one. Agents run alongside humans for weeks before they act unattended.
Ongoing care keeps the model honest
Models drift as reality shifts. The same team monitors accuracy in production, retrains on fresh data and upgrades underlying models as the state of the art moves — FeelEat’s forecasts hold 98% because of that layer, not despite it.
The disciplines behind production AI.
AI / ML engineering
LLM applications (RAG, function calling, agentic workflows), fine-tuning open-weight models when economics favour it, vector databases, evaluation harnesses and MLOps.
AI / ML solutions →AI agents
Tool-using agents with deterministic guardrails, human-in-the-loop approval gates, enforced cost budgets and a full audit log of every tool call and decision.
AI agents →Data engineering
The layer under every model: event capture, pipelines, warehousing and governance that turn scattered operational data into a single auditable source of truth.
Data engineering & analytics →The product around the model
An AI feature ships inside a product, not a notebook — multi-tenant platforms, billing, access control and telemetry engineered by the same team that builds the model.
SaaS app development →AI that survived contact with production.
LeadTrack AI — voice agents at production volume
For LeadTrack AI (Australia) we engineered a multi-tenant voice-agent SaaS end to end: AI agents that call every new lead in under 30 seconds, qualify through natural conversation and hand high-intent prospects to humans with the transcript attached — hardened across more than a hundred thousand live calls.
FeelEat demand forecasting — ML over a live operation
For the FeelEat group we built per-site, per-dish demand forecasting: two years of sales history, seasonality and live signals distilled into a number the operation orders against. The model ran in shadow mode until its track record won the argument — and the platform paid for itself in the first quarter.
Questions teams ask before hiring AI developers
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Q.01What kind of AI developers does CODT provide?
Senior ML engineers and systems architects with 10+ years of production experience, working directly with you — not a rotating bench. The same team covers the model, the data pipelines and the product around them, because production AI fails at the seams between those disciplines, not inside them.
Q.02Have your AI developers actually shipped to production?
Yes — and the proof is linked on this page, not implied. LeadTrack AI, the multi-tenant voice-agent SaaS we engineered, has completed 100K+ live calls with the first call placed in under 30 seconds. The demand-forecasting platform we built for the FeelEat group runs at 98% accuracy in production and paid for itself in the first quarter.
Q.03Which models and stack do you work with?
Frontier models (Anthropic Claude, OpenAI) with strong prompting and retrieval first — for most business use cases that beats fine-tuning on speed, cost and quality. We fine-tune open-weight models (Llama, Mistral, Qwen) when latency, cost-per-call or data privacy demands it, and we always benchmark against the frontier baseline before committing.
Q.04How do you keep AI costs under control?
Aggressive caching, tier-routing (cheap model first, escalate only when needed), prompt compression and per-tenant budget guards — we’ve cut customers’ inference bills by 70%+ without measurable quality loss. Every agent runs under an enforced cost and latency budget per task.
Q.05Is our data safe with an external AI team?
Yes. We default to enterprise tiers of model providers — zero data retention, no training on your prompts — and for maximum privacy we deploy open-weight models on private infrastructure. Ownership is 100% yours from day one: the code, the weights and the pipeline.
Q.06How fast can AI developers start?
A senior engineer reviews your enquiry and replies within one business day, and agent engagements open with a paid one-week scoping spike. For build timelines the site’s own service pages are the reference: a single-purpose internal agent typically takes 4–6 weeks, a customer-facing agent with multiple tools and approval flows 10–16 weeks.
Q.07Where does the AI team sit?
CODT’s engineering hub is in Gurugram, India, with a North American office in Milpitas, California — follow-the-sun coverage across time zones, with stand-ups and demos on your clock and collaboration in English, French and German.
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