Engineering consultancy · From architecture to production

Data, AI and software engineering for complex problems.

Liquid Chair designs and builds production data platforms, applied AI systems and custom software. Our engineers work close to your teams, data and operating environment from architecture through deployment.

Data EngineeringApplied AI & Machine LearningSoftware EngineeringForward Deployed Engineering

Problems we solve

The work usually starts where systems, data and decisions are messy.

Buyers rarely need a capability in isolation. They need a difficult operational problem understood, engineered and put into use.

Fragmented operational data that prevents confident decisions
Pipelines and integrations that are unreliable or difficult to scale
AI prototypes that have not become production capability
LLM initiatives disconnected from real workflows and data
Legacy applications slowing delivery or constraining product work
Complex integrations across software, data, and business systems
Products that require software, data, and AI to work together
Ambiguous initiatives where requirements emerge through close collaboration

How we work

A direct path from context to production.

We start with the real problem, shape an architecture around the environment, build in useful increments, deploy into practice, and learn from use.

  1. 01

    Understand

  2. 02

    Design

  3. 03

    Build

  4. 04

    Deploy

  5. 05

    Iterate

See our delivery approach

Forward Deployed Engineering

Engineers close enough to turn ambiguity into working systems.

Experienced engineers work directly with the people, systems and data around a complex initiative. Short feedback loops turn emerging requirements into software, data and AI solutions that fit the actual environment.

This is a hands-on delivery model, not generic staff augmentation and not consulting from a distance.

Works inside the operating context

Stakeholders

Users

Data

Systems

01

Embedded context

Engineers work near the people, workflows and constraints that define the real problem.

02

Technical translation

Ambiguous needs become architecture, interfaces, data flows and production increments.

03

Production accountability

The work stays connected through integration, deployment and learning from actual use.

Explore Forward Deployed Engineering

Product evidence

We build what we advise on.

Lodgegrid helps travellers stop comparing hundreds of hotels. It turns stay preferences, location needs and repeated guest-review patterns into a smaller shortlist and an explained recommendation.

For Liquid Chair, it is product evidence: software, data, applied AI and product engineering working together in a live public service.

Explore Lodgegrid

Lodgegrid

Hotel decision intelligence

Review intelligence

Quiet rooms92%
Breakfast81%
Room size70%

Recommended pick

The Annex House

A clear best-fit hotel with plain reasons tied to location, comfort and stay preferences.

9.2

Why it fits

Evidence before booking

Highlights the strengths, weaknesses and trade-offs guests repeatedly mention.

Fit

Watch out

Practical trade-offs

Calls out the details that matter before booking, not after arrival.

Read

Software engineering

A live decision-support web product with user-facing workflows, comparison flows, and production delivery concerns.

Data engineering

Hotel, location, price, rating, and guest-review information organized into usable decision context.

Applied AI

Review-theme and semantic understanding used to help travelers compare tradeoffs instead of scanning raw listings.

Product engineering

A real system Liquid Chair builds and operates, improved through the realities of product use rather than slideware.

Why Liquid Chair

Credibility comes from what gets engineered, integrated and operated.

The strongest public proof available on this site is Liquid Chair's own product work. Additional client outcomes should be added only when they can be described accurately and with permission.

01

Architecture through deployment

The work is framed as engineering delivery, not advice that ends before integration and production use.

02

Business context in the engineering loop

Users, workflows, data, constraints, and existing systems shape technical choices while the work is being built.

03

Product evidence

Lodgegrid demonstrates that Liquid Chair applies software, data, AI, and product thinking to a product it operates.

Next step

Bring us the problem that needs to work in practice.

Tell us what you are trying to build, improve, or put into production.