At Altura, we provide AI and automation software to help companies win complex public and private sector tenders and RFPs.
Our customers are companies in industries like construction, IT and consultancy, where winning competitive tenders is one of their main revenue streams. For those selling goods and services to national or local government (a market worth around £395 billion in the UK annually), tendering is often the only option.
We help companies tackle the inefficiencies of managing complex bidding processes, which involve many manual, repetitive tasks that are prone to errors and collaboration bottlenecks. Studies suggest businesses lose up to 25% of potential revenue due to inefficient bid management.
Our platform significantly reduces manual effort, supports better decision-making, and improves win rates by enabling companies to focus on strategy, instead of admin. We centralise data to extract insights with AI and automate time-consuming repetitive activities.
My co-founder, Jordi van der Hek, and I were driven to create Altura to address a clear market need. It all started when l saw a client in a previous company consistently struggling with a variety of complex, manual tender and RFP processes. I was also struck by the vast amount of valuable data available in the bid process that was being left on the table.
Since our launch in the Netherlands in 2021, Altura’s goal has always been to help companies streamline those processes, enhance collaboration between the various stakeholders and subject matter experts whose input and feedback is required to prepare bids, and optimise outcomes by applying AI to contextual data for better decisions.
We’ve now expanded our European presence with a London office and are continually refining our technology, while working on introducing autonomous AI agents. The agents go beyond traditional AI tools, not just assisting users, but actively completing tasks and making decisions on their behalf, solving some of the most time-consuming and complex challenges bid managers face today.
We give bid teams a central environment: one place where decisions are being made. AI tools powered by Large Language Models (LLMs) assist with tasks such as searching internal data, summarising complex information – including lengthy tender documents – and generating high-quality proposal content.
The software automates time-consuming workflows like reviewing and summarizing tenders, delivering insights to guide bid/no-bid decisions and analysing performance on past bids to improve future win rates.
Our AI agents can map what’s required for each tender and automatically answer or assign the right experts. They can learn from interactions with the experts and draw on this knowledge to draft answers, flag risks in tenders and surface win-loss insights.
The most significant mindset change involves people becoming comfortable thinking of AI as a helpful co-pilot rather than either a threat to their job, or that the AI will do everything for them. They also need to switch from thinking about every bid as a one-off project to viewing bidding as a set of repeatable, data-driven processes. To become AI-enabled, you have to turn bid content and knowledge into shared assets for AI to be applied to.
For example, if they don’t already exist, you need to create documented processes for every stage of the bidding cycle for AI to scale. Is there a rationale for deciding when you should bid for a tender or not, for example? Or agreed criteria for evaluating past tenders?
AI is only as good as the information you feed it. So, a central folder for all bid content is another essential. Past bids, templates, case studies, pricing information, policies, CVs and more need to be accessible to the team and to the AI.
The shared knowledge and content are the foundations your AI tools lean on to automate and inform daily workflows.
As well as completing tasks faster and more efficiently, we look to use AI to do things that were not possible or practical before. Like being able to analyse hundreds of past tenders and proposals across multiple clients to determine the optimal strategy for a current bid. That level of insight and strategic depth simply wasn’t feasible before AI, given the sheer volume of data and time constraints involved.
So instead of only tracking efficiency gains, we calculate ROI based on increasing our customers’ win-rates. We want our AI to help them increase the quality of their work. And with quality improvements, you can see higher win rates, which translate into revenue growth and a positive ROI.
AI has yet to make its biggest impact on bid management. So far, it has been used to summarise, analyse and provide outline responses. And many companies have focused heavily on trying to write tender proposals with AI, since this seemed the most obvious solution.
In the future, we will see two fundamental shifts. First, companies will need to create structured data about tenders, past bids and internal systems and more – and establish a central source of truth to stay competitive. Having all data unified and structured will be the key differentiator when leveraging AI, since the underlying models will be the same for every company.
Second, we will shift from using generic LLMs in chat interfaces to relying on specialised workflows. This is where agents come to life – analysing vast amounts of information, structuring it to keeping humans in the loop, and automatically producing the expected outputs. These workflows will be purpose-built, for example to answer requirements, mitigate risk, make bid/no-bid decisions, and more.
But the idea that AI will write a winning tender for you is quickly disappearing. Because people are realising that AI can’t replace human insight, creativity and judgment. Instead the focus is shifting to “how can AI save me time, to help me focus on the strategy that can help us win”.
