AI developers who put models into production.
CODT Technologies’ AI developers have already put two systems into production: the voice-agent SaaS behind 100K+ live calls and a demand-forecasting platform that holds 98% accuracy. The evals, guardrails and drift monitoring that keep a model accurate after launch come with the team, and you work with the engineers directly.
We check the problem before we pick a model.
A paid discovery sprint
The sprint first checks whether ML is needed at all, or whether a simple rule would do the job cheaper and more reliably. It frames the problem, checks your data and writes down what the system must never do, before anyone touches a model.
A costed plan before any code
Before any code is written, you get a costed technical plan: scope, architecture and a clear list of what we will not build. The price is for the outcome, and cost and timeline are settled up front.
A senior AI team builds eval-first
Every prompt and model change is tested against a frozen evaluation set, with cost guards and quality monitoring from the first build. Agents run alongside people for weeks before they act unattended.
Ongoing care keeps the model accurate
The same team watches accuracy in production, retrains on fresh data and moves to better underlying models when they appear. That routine work is what keeps FeelEat’s forecasts at 98%.
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 solutionsAI 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 agentsData 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 & analyticsThe product around the model
Multi-tenant platforms, billing, access control and telemetry around the AI feature, built by the same team that builds the model.
SaaS app developmentTwo AI systems in production.
LeadTrack AI — voice agents at production volume
For LeadTrack AI (Australia) we engineered a multi-tenant voice-agent SaaS end to end. Its AI agents call every new lead within 30 seconds, qualify through natural conversation and hand high-intent prospects to a person with the transcript attached. More than 100,000 live calls have gone through it.
FeelEat demand forecasting — per site and per dish
For FeelEat we built demand forecasting per site and per dish, from sales history, seasonality and live signals. It gives the operation one number to order against.
Questions teams ask before hiring AI developers
Opisz to w briefie. Odpowie doświadczony inżynier — nie handlowiec — w ciągu jednego dnia roboczego.
What kind of AI developers does CODT Technologies provide?
Senior ML engineers and systems architects with 9+ years of production experience, who work with you directly. The same people handle the model, the data pipelines and the product around them, because production AI usually breaks where those pieces meet.
Have your AI developers actually shipped to production?
Yes, and both case studies are linked on this page. LeadTrack AI, the multi-tenant voice-agent SaaS we built, has completed 100K+ live calls, with the first call placed within 30 seconds. The demand-forecasting platform we built for FeelEat runs at 98% accuracy in production.
Which models and stack do you work with?
We start with frontier models (Anthropic Claude, OpenAI) and good prompting and retrieval. For most business use cases that works better than fine-tuning and costs less. We fine-tune open-weight models (Llama, Mistral, Qwen) when latency, cost per call or data privacy demands it, and we benchmark against the frontier baseline before committing.
How do you keep AI costs under control?
We cache aggressively, send each task to the cheapest model that can handle it, compress prompts and set budget limits per tenant. With these steps we’ve cut customers’ inference bills by 70%+ without measurable quality loss. Every agent runs with a hard cost and latency budget per task.
Is our data safe with an external AI team?
Yes. By default we use the enterprise tiers of model providers, with zero data retention and no training on your prompts. For maximum privacy we deploy open-weight models on private infrastructure. Once you have paid, the code, the weights and the pipeline are 100% yours.
How fast can AI developers start?
A senior engineer replies within one business day. Next comes a paid discovery sprint of 1–2 weeks, which ends in a written scope and a fixed quote and is credited in full against the build. The build team starts 4–5 business days after you sign the quote. Our service page gives a reference build time: a customer-facing agent with several tools and approval flows takes 10–20 weeks; a single-purpose internal agent takes less.
Where does the AI team sit?
In CODT Technologies’ engineering hub in Gurugram, India. The development team and its project manager work on your time zone, in English, French or German.
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