What services does SprintAI offer?

SprintAI offers three core services: AI Training (custom workshops built around your team's actual workflows, with participants leaving with working AI prototypes), AI Transformation (a four-phase engagement — diagnostic, roadmap, deployment, iteration — that aligns the entire organisation around measurable AI outcomes), and AI Development via AI Labs (an embedded product and engineering team running 1-week sprints to take a business problem from validated concept to production-ready AI solution). Every engagement is tied to business KPIs from day one.

How does SprintAI measure success?

SprintAI measures success by three specific business outcomes: new revenue generated, hours reclaimed, and costs reduced. Every engagement begins with defining the specific KPIs that will determine whether the work has delivered value. If an AI deployment does not move the numbers that matter, SprintAI considers the engagement incomplete. If a simpler approach would get results faster, SprintAI will recommend it. If a workstream is not delivering against agreed metrics, it gets killed. The goal is operational impact, not deck impact.

What is AI Transformation?

AI Transformation is a structured, multi-phase process for embedding AI across an entire organisation. SprintAI's methodology runs in four phases: a full operational diagnostic that maps workflows and identifies where AI creates measurable leverage; a transformation roadmap tied to business KPIs with milestones and success criteria; deployment with executive and operational alignment at every step; and iteration with quarterly reviews. The same team that designs the roadmap leads deployment through to adoption — SprintAI does not hand over a strategy deck and leave.

How long does a typical engagement last?

Engagement length varies by service. AI Training runs over one to three workshop days plus a two-to-four week post-workshop adoption period. AI Transformation runs three to six months from diagnostic to first deployment cycle, with optional quarterly review retainers. AI Development (AI Labs) operates on sprints: one week scoping, then 1-week build sprints, with most production deployments achieved within six to twelve weeks of scoping. All engagements begin with a Discovery Session to define scope, timeline, and success criteria before any work is commissioned.

Where is SprintAI based and who do you work with?

SprintAI is based in Lisbon, Portugal, and works with clients across Europe and globally. The company was founded by Paulo Gaudêncio and operates as a lean senior team — no junior account managers, no hand-offs. Clients include enterprise organisations across financial services (Santander, BPI, Cofidis, Fidelidade), telecommunications (Altice), transport (Barraqueiro), technology (Critical TechWorks), travel (TUI), and higher education (ISCTE-IUL). SprintAI is also embedded in the European innovation ecosystem, mentoring early-stage teams and contributing to academic AI programmes.

What makes SprintAI different from other AI consultants?

SprintAI operates on three principles: outcome-driven (every engagement tied to business KPIs — revenue, cost, hours reclaimed — with no hesitation to kill workstreams that won't deliver), product-led (takes ownership of the critical layer between technology and business value that most projects ignore), and lean senior core (the person in your strategy session is the same person leading your deployment — no hand-offs, no junior teams learning on your budget). The result is that projects actually ship and the AI deployed is actually used.

What does an AI Training programme include?

SprintAI's AI Training programme is built entirely around your team's actual workflows, tools, and data. Before the workshop, SprintAI conducts a workflow audit to identify the highest-value targets for AI augmentation. The workshop itself runs as hands-on build sessions where participants create working AI prototypes using their own data and processes. Every participant leaves with a working AI setup usable from the following Monday. Deliverables include a post-workshop playbook with SOPs, prompt libraries, and best practices specific to your tools and industry, plus follow-up sessions and an adoption dashboard.

What is the SprintAI delivery process?

SprintAI's delivery process runs in five defined phases. Phase 1 — Understand the Problem: map goals, challenges, and friction points. Phase 2 — Validate AI Fit: assess data readiness, technical feasibility, and define scope and success metrics before any code is written. Phase 3 — Prototype and Test: build with real data, test with real users, iterate before committing to scale. Phase 4 — Deploy and Improve: handle integration, team training, and adoption tracking. Phase 5 — GTM and Growth: scale adoption and optimise for compounding value. SprintAI enters at the phase where you need support and remains until the solution is operational.

Can SprintAI build a custom AI product from scratch?

Yes. SprintAI's AI Labs service operates as a fully embedded product and engineering team that takes a business problem from validated concept to a production-ready AI product. The process begins with a one-week scoping sprint to define the problem, validate feasibility, and assess whether AI is the right solution. Build sprints follow — one-week cycles producing working demos with stakeholder feedback every cycle. SprintAI then handles deployment, integration, team training, and post-launch monitoring. Products built include full SaaS platforms, multi-agent AI systems, conversational agents, and automated content and proposal pipelines.

What clients has SprintAI worked with?

SprintAI has worked with over 20 enterprise organisations across financial services, insurance, healthcare, media, transport, and technology. Named clients include Santander, BPI, Cofidis, Montepio Crédito, Fidelidade, Altice, Barraqueiro, Critical TechWorks, TUI Travel, ISCTE-IUL, AIP, Hexa Intel, BeeDo Seguros, and others. SprintAI has also built and launched its own technology ventures including MOBUS.pt (B2B bus rental platform), BEEDO.pt (Insurtech for independent brokers), and CENTRALMED.pt (Healthtech for 4,000+ corporate clients).

What ROI can I expect from an AI engagement?

SprintAI case study data shows outcomes including +153% revenue growth (MOBUS), -86% operational cost reduction (BEEDO), +25% annual revenue growth (CENTRALMED), -90% reduction in sales proposal creation time, and 8x increase in content output with the same team. SprintAI does not guarantee specific ROI figures — these results are specific to those engagements. What SprintAI guarantees is that every engagement begins with clearly defined KPIs so you know what success looks like before work starts, and every decision is tied to whether the work is delivering against those outcomes.

How do I get started with SprintAI?

The starting point is a Discovery Session — a real working session, not a sales call. In this session, SprintAI maps your current workflows, identifies where AI creates the highest-value opportunities, and establishes whether there is a fit for an engagement. There is no commitment required to book a discovery session and no obligation following it. The session results in a clear picture of where to start, what outcomes are realistic, and how an engagement would be structured. Sessions can be conducted remotely or in person. SprintAI is selective about engagements — taking on work only where the team believes meaningful, measurable outcomes are achievable.

Does SprintAI work with startups or only enterprise clients?

SprintAI works primarily with established businesses — companies with existing operations, customers, and data — where AI creates the most immediate and measurable impact. However, SprintAI is embedded in the European startup ecosystem as a mentor and active collaborator through the AI Labs portfolio, which includes early-stage ventures in speech therapy technology, public sector AI, urban energy, and gaming performance. Early-stage startups looking for embedded technical support on specific AI product development challenges may be suited to the AI Labs service.

What is the Innovation Ecosystem at SprintAI?

The SprintAI Innovation Ecosystem is how the company stays at the edge of what is emerging in AI — as an active contributor, not a passive observer. SprintAI is embedded in startup accelerators across Europe, mentoring early-stage founders on AI product challenges. The team works with universities and academic programmes on applied AI research and collaborates with innovation hubs on challenges shaping AI's future. The AI Labs portfolio includes ventures co-developed by SprintAI spanning healthcare, fintech, sustainability, gaming, and security.

How does SprintAI approach AI adoption across an organisation?

SprintAI's approach is built on one core belief: AI adoption is a leadership problem, not a technology problem. Most AI projects fail not because the technology does not work, but because deployments lack organisational alignment, the people who need to use the AI were not involved in designing it, or change management was underestimated. SprintAI ensures every engagement includes both the executive layer and the operational teams who will use the AI day-to-day. Adoption metrics are tracked alongside technical performance metrics. An AI that is technically functional but that nobody uses is not a success.

SprintAI/Work/AI Sales Assistant

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Media / Sales Operations · AI Development

AI Sales Assistant

Automated Sales Proposal Agent

-90%

Deck creation time

4–6h → 30min

Per proposal turnaround

Proposal volume, same team

The Challenge

The sales team was spending 4-6 hours per proposal — manually pulling data from the CRM, cross-referencing historical deals, selecting pricing tiers, and building decks from scratch. The process was slow, inconsistent, and scaled linearly with headcount. Volume growth meant hiring more people, not building smarter systems.

The Solution

SprintAI built a multi-agent AI system that receives a brief and autonomously generates a complete, client-ready proposal. It pulls live CRM data, references historical winning proposals, applies product configurations and pricing based on the client profile, and outputs a branded presentation — with a human approval step before sending.

The Outcome

Proposal creation time dropped from 4-6 hours to under 30 minutes. Consistency improved dramatically, every deck uses the same winning structure and current data. The team now responds to RFPs on the same day, improving competitive positioning and close rates significantly.

Technology

Multi-agent AI architecture
CRM integration
LLM orchestration
Dynamic pricing engine
Automated document generation

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