We build AI agents. We build them with AI agents.
SoftmaxAI designs and provides AI agents, sophisticated chatbots, voice AI, and AI-native products by means of a specification-driven method, accelerating each phase of product development through the use of AI, having all decisions approved by a human, and ensuring that everything is delivered strictly in accordance with the specification. 100+ projects for 50+ clients worldwide.
Five disciplines, one standard: AI that survives contact with production.
AI Agents & Agentic Workflows
Agents that plan, execute, and finalize multi-step tasks within your real systems — not merely in demonstrations — such as those involving multi-agent orchestration, MCP integrations, and agents that use tools and are linked into your CRM, ERP, and data stack.
Explore AI agent development →AI Chatbots & Voice AI
From website assistants to phone-line voice agents, chatbots and voicebots that are able to have real conversations—specifically, ones that are multilingual, context-aware, and connected to your backend systems—are available and serve to resolve issues rather than simply deflecting them.
Explore chatbot & voice AI →Generative AI & RAG
The various applications of LLMs developed from your own data—such as enterprise knowledge systems, document intelligence, fine-tuning, and GenAI audits—include evaluations which demonstrate that the answers are correct.
Explore generative AI →AI-Native Web & Mobile Apps
The product is designed using an AI-first method, taking it through from the MVP stage all the way to large-scale deployment; we develop both the application and the intelligence contained in it, everything being carried out by a single team based on specifications and sped up by means of agents.
Explore product engineering →ML, Computer Vision & Cloud MLOps
Deep-tech depends on the development of custom machine learning models, the use of vision systems for detection and inspection, and GCP/AWS infrastructure which has been designed so that the models stay fast, are constantly monitored, and remain affordable.
Explore ML, vision & cloud →Spec-driven. Agent-accelerated. Human-owned.
The softmax function turns noisy signals into a single definite decision; which is the reason it figures in our name and is also the approach we take when managing each project, employing four stages and requiring you to approve the specification before any code has been written.
Spec
The idea is set down in writing as a specification, covering details concerning the architecture, the constraints, the edge cases, and the acceptance criteria. You then examine and approve it, ensuring that there are no vague tickets and that no surprises arise later on.
Build, with agents
Our engineers set up AI agents so that all the different stages—scaffolding, implementation, testing, and documentation—are carried out in parallel; this is the reason why deliveries are made within weeks. We do not just build agentic AI; we build using it.
Verify
The evaluations, automated tests, and security checks are carried out together with the build, not after it; each change can be traced to a specific line in the specification and all major decisions are submitted to human review.
Ship & improve
You can deploy it on Google Cloud or AWS while incorporating monitoring, production evaluations, and retraining loops. The specification then acts as living documentation that your team can inherit—there is no lock-in and no black box.
Built for what happens after the demo.
Production-first engineering
Putting together a demo is easy, but our emphasis is on what follows this—on monitoring, retraining, and cost control as well as on scaling.
Google Cloud Partner depth
Engineers who are certified, have early access to the Gemini and Vertex AI platforms, and whose architectures have been validated against Google's own best practices.
Senior engineers, direct access
There is no account manager level; you interact directly with the ones who prepare the specifications and who review the code.
Full-stack AI capability
The data, the models, the infrastructure, and the product itself — all managed by one team from beginning to end, with no hand-offs between vendors.
Honest scoping
In each case where a simpler solution turns out to be better than the use of AI for your problem, we will note that.
Security by default
The system provides encryption, ensures access on the basis of the principle of least privilege, adheres to regional data residency where required, and features a compliance-aware architecture.
Real problems, shipped systems.
A voice agent that onboards patients and books appointments
A conversational voice agent that welcomes callers, tells whether the patient is new or returning, leads new patients through the onboarding process, and arranges appointments from start to finish — so routine calls are handled in a natural way and staff can concentrate on the interactions that require a human.
Read case study →Technology & SaaS · GenAI & RAGA knowledge-grounded assistant that speeds up support resolution
An internal support assistant which enables employees to resolve technical problems by asking questions in plain language and receiving answers based on the organization's own documentation, rather than having to go through error logs and refer to dispersed sources.
Read case study →Manufacturing · ML & VisionVideo analytics that reveal and remove assembly-line bottlenecks
LineSense is an AI-powered video analytics platform which constantly monitors workstations and converts motion data into objective and unbiased insights, providing manufacturing teams with a factual, up-to-date view of where time is being lost on the production line.
Read case study →A Google Cloud Partner for the AI era.
The fact that we have a partnership with Google entitles us to have certified engineers and architectures that have been tested according to Google's own best practices, as well as giving us early access to the Gemini and Vertex AI stack — which is why the solutions that we are building for you at the moment will be ready for whatever Google launches tomorrow. Furthermore, since we run production workloads on AWS, the recommendation you get is the one that is appropriate for your situation, not the one that is convenient for us.
Explore our Google Cloud partnership →Forward-deployed engineers, not remote consultants.
It has been found by the industry that AI functions most effectively when engineers take the lead by becoming part of the customer's team, working within their systems and applying it to their real data. Currently, the biggest AI companies in the world are expanding on this approach, Google Cloud having established forward-deployed engineering hubs in India. That has always been our way – SoftmaxAI engineers attend your standups, work in your repositories and in your cloud environment, and stay responsible for the outcomes in production, wherever in the world your team is located and whatever its time zone.
We set up our infrastructure within your cloud, your repositories, and your compliance boundaries.
The times when you're working overlap in a way that corresponds to the location of your team, covering your standups, your Slack messages, and your sprint cadence.
Being forward-deployed means we're still there on Monday morning, when the model meets real production traffic.
Advise. Build. Partner.
AI Opportunity Sprint
It's a brief and clearly defined engagement—we'll audit your data and systems, identify the AI workloads that offer the most value, and provide a technical specification that is ready for implementation. Even if you carry out the building elsewhere, you'll still have the blueprint.
Spec-driven delivery
If you have a well-defined problem, we will handle it throughout the process using the SoftmaxAI Method — with a clear scope, weekly demonstrations, and working software available during the first sprint.
Dedicated AI team
If you need actual AI capabilities, not just a project, then have engineers who are forward-deployed and based within your company — a dedicated team that develops in line with your roadmap.
“Working with SoftmaxAI on our AI project was an exceptional experience. Their expertise in Python and AI, particularly in chatbots and prompt engineering, was instrumental in enhancing our SaaS product. Their skillful use of the OpenAI platform and adept integration of the Pinecone vector database showcased impressive technical abilities. Beyond technical skills, their collaborative approach and commitment were highly commendable.”
“SoftmaxAI has been an absolute pleasure to work with. We’ve worked on multiple projects together at a high complexity level, and the team was able to handle everything seamlessly. When you find a great partner, you know it, and we will continue working together for the long term. Thanks team!”
“SoftmaxAI and her team did an amazing job. I would strongly recommend her to anyone who needs speed of execution and a trustworthy collaboration. Thanks!”
AI, applied to your industry.
Voice agents help with patient intake, support diagnosis, and handle admin tasks automatically.
FinanceFraud detection, risk scoring, and market forecasting.
Retail & E-commerceRecommendations, demand forecasting, and visual search.
ManufacturingVisual quality checks, predictive maintenance.
LogisticsRoute planning, delay prediction, document automation.
Technology & SaaSCopilots, AI search, and agentic features built into your product.
The people behind the agents.
FAQ
What does spec-driven development mean?
Do you use AI to write code? Who reviews it?
What is the Model Context Protocol (MCP), and how do you use it?
How do you keep our data secure?
How long until we see results?
Will this work with our existing systems?
Do your engineers overlap with our time zone?
Who owns the code and models?
What happens after launch?
Stop guessing your AI timeline. Engineer it.
Begin with a fixed-scope AI Opportunity Sprint and end up with a build-ready specification—otherwise, speak to an engineer first. In either case, you'll receive an honest assessment of feasibility, not a sales pitch.