Most AI projects stall between pilot and production. We close that gap. Kansoft delivers AI readiness assessments, agentic workflows, RAG-powered applications, and the MLOps backbone needed to run them safely at enterprise scale.
The hard part of enterprise AI isn't the model — it's the data, the guardrails, and the production discipline around it. We engineer for all three.
Demos that never make it past a sandbox because no one owns hardening, evaluation, or rollback.
Models trained on dirty pipelines fail silently in prod. We treat data quality as a first-class deliverable.
Hallucinations, prompt injection, and PII leakage need defences — not after launch, but by design.
Without baselines, eval harnesses, and KPIs wired in, you can't prove value or course-correct.
Engage with one practice or compose them end-to-end — strategy through deployment, all delivered by the same engineering team.
Readiness assessment, use case identification, roadmapping
Explore practiceProduction-grade autonomous agents and workflows
Explore practiceLLM integration, RAG pipelines, custom models
Explore practiceAI woven into every stage of the SDLC
Explore practiceModel deployment, monitoring, and scaling
Explore practiceEvery Kansoft AI engagement starts with a fixed-scope readiness sprint. By week four you have a prioritised roadmap, a reference architecture, and a decision on what ships first. By week twelve, that first use case is in production.
Data audit, use-case scoring, governance gap analysis, and architecture blueprint — all inside a 4-week fixed sprint.
Deliver one prioritised use case end-to-end with the eval harness, observability, and human-in-the-loop wired in from day one.
Red-team the pipeline, plug guardrails, define rollback, and ship behind feature flags with measurable KPIs.
MLOps automation, monitoring, drift detection, and a roadmap for the next two to three use cases.
Vendor-neutral by design — we pick the right stack for your data, latency, cost, and compliance constraints.
Credit risk scoring, fraud detection, document intelligence, and regulatory reporting agents.
Clinical decision support, imaging triage, and revDSG- and GDPR-compliant patient-facing assistants.
Personalisation, demand forecasting, conversational commerce, and dynamic pricing.
Predictive maintenance, quality vision, and supply-chain optimisation.
Contract analysis, knowledge agents, billing automation, and proposal generation.
Grid optimisation, consumption forecasting, and ESG reporting copilots.
One engagement shows what we mean by production-grade AI — not a demonstrator, but a system carrying planning decisions every day.
We're not an AI lab. We're a software engineering firm that builds AI products the way we build the rest of your systems — observable, testable, and accountable.
We don't resell platforms. Recommendations follow your data, latency, and cost — not partner margins.
EU AI Act, GDPR, revDSG, SOC 2 Type II, and Swiss/EU data residency baked into the architecture from day one.
Every pilot ships with a regression suite, golden datasets, and KPIs — not just a demo.
CI/CD for models, drift detection, canary rollouts, and rollback — the same rigour as your application stack.
Your team owns the runbooks, the prompts, and the pipeline. We build capability, not dependency.
A Swiss entity you can hold responsible, with delivery centres in India. Working hours overlap with CET core hours, so coordination, reviews and demos happen the same day.
What companies ask before they start with AI.
With a use case, not a technology. We usually start where time is measurably lost today, most often in document processing. A small pilot shows within a few weeks whether the benefit holds, before you invest further.
Yes. We run models in Swiss or EU regions, or inside your own environment, and your data does not train public models. What goes where is recorded in writing, so you can evidence it to clients and regulators.
Reliable enough when a person stays in the decision. We build systems with traceable sources, review steps and sign-offs at the points where an error costs money or trust. Before go-live we measure quality against your own cases.
It depends on the use case, and we give the range in the first call. What drives the price is the integration with your systems and the state of your data, rarely the model itself. A scoped pilot is kept deliberately small, so deciding on it is not a decision of principle.
We set the architecture and documentation up for it from the start — classifying the use case, keeping data provenance traceable, logging, and human oversight. For Swiss clients, revDSG and, where relevant, FINMA requirements come on top.
It is yours: code, prompts, configuration and documentation. We build on abstraction layers so you can change the model later without rewriting the application.
