NewThe AI-Native Playbook for Enterprises

Rebuilding companies into AI‑native businesses

Twistag is a Portugal-based applied AI company. We design, build and run the agents and data systems that put AI to work in mid-market and enterprise companies.

What changes for the business

AI-native companies do more with the team they have.

Twistag works with leadership to rebuild how work gets done. Agents take the volume, people keep the judgement, and the business grows without headcount growing at the same pace.

The AI-native advantage
  1. Margins that hold as volume grows
  2. Decisions made on today's numbers
  3. Teams focused on customers and judgement
  4. Capacity added in weeks
  5. Systems the company owns outright

Twistag designs, builds and runs it alongside leadership, one proven workflow at a time.

Since 2016

A decade of systems companies run on every day.

  • AWSPartner
  • Clutch, rated 4.9 out of 5
  • Nike
  • Autodesk
  • Bangkok Bank
  • Worten
  • kencko
  • DataTalks
  • Defined.ai

Most of what a company spends goes on work that moves between people and systems: reading, checking, matching, chasing. AI can now carry much of that work. Making it hold up inside a real business, with real data and real accountability, is what we do.

How we work
Where it shows up

Where the work changes first.

Operations

Staffing planned against real demand.

Occupancy forecasts move people between nearby sites before outside agencies are called. Managers approve the plan; the agent does the arithmetic.

70%less agency staffing spend for a European hotel group
A hotel reception desk seen through the glass entrance door, trees reflected over it
Customer service

Replies drafted in the company's own voice.

Every message is classified, the account pulled up and a reply drafted against house guidelines. A person approves it in one click.

Everydraft checked for a UK water utility
A customer service advisor with a headset seen through a frosted glass partition
Compliance

Every launch checked, market by market.

Ingredient and label rules are applied country by country, with each finding tied to the regulation behind it, before a product ships.

75%less diligence effort for a European RegTech
Hands holding a small glass cosmetic jar up to the window light
Practices

Data, agents and products, designed together.

AI projects stall in the gaps between vendors.
We cover the full path, so nothing waits on a handoff.

01

Agents that plan, decide and act across company workflows, with people approving what matters and every decision traced.

  • Agentic workflows
  • Multi-agent systems
  • Search over company knowledge
  • Human review steps
Explore AI & Agents
02

Data platforms agents can actually read: clean, current and connected to the ERP and CRM, on infrastructure the business controls.

  • Data platforms and pipelines
  • Data lakes and warehouses
  • Data governance
  • ERP and CRM integration
Explore Data & Cloud
03

AI-native products and internal tools, and the platforms around them, designed, shipped and run by the same team.

  • Web and mobile products
  • Internal tools and portals
  • API design
  • Legacy modernisation
Explore Product Engineering
A glass lens resting on a grid of blank cards, magnifying one of them

Find the work closest to the challenge.

Answer two questions to see the most relevant project, service and reading.

1. IndustryQuestion 1 of 2
Approach

Commitment grows only after proof.

  1. Find the workflow

    Map where work waits, what fixing it is worth and which number will prove it, with the people who run the process.

    2–4 weeks
  2. Prove it

    One workflow rebuilt around agents on real data, and tested against that number before anything scales.

    6–8 weeks
  3. Scale it

    Each new workflow builds on the same foundation, so every one ships faster than the last.

    Quarters
  4. Run and hand over

    Improved from real use, then handed over. Code, models and tests stay in the company’s own cloud.

    Ongoing
Ways to work together

Three ways to bring Twistag into a plan that is already set.

Prove it

Fixed-scope
project

Best forCompanies with a clear problem, a budget and a date: one system, built to spec and handed over.

Send a project brief
Scale it

Forward Deployed
Engineering

Best forAn approved roadmap that needs senior engineers who think like operators, inside the company’s own teams.

Explore FDE
With partners

Partners and
portfolios

Best forCompanies that build AI, data and product with Twistag: tech companies, consulting and PE firms.

Explore a partnership
Industries

Built for the companies that keep things running.

Thinking

Our thoughts on AI, data and product.

Short, practical reading on putting AI to work, from the team doing it every day. All thinking

Featured · Playbook

The AI-Native Playbook for Enterprises

How leadership teams choose the first workflows, prove the value and scale AI across the business. Written from Twistag engagements.

AI & Agents

Five engineering reasons AI agents fail once live

Delivery & Process

Cut process complexity before adding AI agents

AI & Agents

How long does it take to ship an AI agent to production?

Newsletter

One email a month on what is working with AI inside real companies.

Questions

What leadership asks first.

Why Twistag?

The people in the first meetings are the people who build. Data, agents and product sit in one team, so nothing waits on a handoff between vendors, and the work is measured against a business metric agreed with leadership at the start.

What happens when an agent gets something wrong?

Confidence thresholds and approval rules are set with the process owner. Anything below the threshold goes to a named person, and every run and approval is logged. Leadership, risk and IT sign off on the guardrails and the audit trail before anything goes live.

Where does company data go?

Data stays in the company's own cloud or on-premises infrastructure. Personal data is covered by a data processing agreement under the GDPR, and the company owns all code and IP.

Does it fit the existing stack?

Twistag works inside the company's repositories, CI and cloud accounts, and connects agents to the ERP, CRM and ticketing systems already in place.

How quickly do results show up?

A team is in place two to four weeks after signature. The first system runs on real data within six to eight weeks, measured against the business metric agreed at the start.

What does the proof cost, and what if it fails?

The proof phase is scoped and priced up front, so the risk is known before work starts. If it doesn't move the agreed metric, the work stops there, and the company keeps the code, the data and the findings.

How does the EU AI Act apply?

Each use case gets a risk classification and the controls it needs. Each carries a recorded risk tier, a named person responsible for oversight and a log of every run and approval, which is the material the Act asks deployers to keep.
Talk to us

Bring AI into the work of every team.

A conversation on where agents add capacity, what it takes to build them, and which number shows it is working.

A senior lead replies within one working day.

Talk to us

Tell us where work gets stuck.

A senior engineer replies within one working day.

Interested in

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