How do outsourced teams collaborate with internal teams?

Outsourced product teams typically integrate into existing workflows through agile processes, shared project management tools, and regular communication routines. Sprint planning, demos, and continuous feedback cycles allow internal and external engineers to work as a single product team.

How long does it take to build an MVP?

The timeline for building a Minimum Viable Product depends on product complexity, feature scope, and team size. In many cases, an MVP can be designed and developed within three to six months.

What industries do you typically work with?

Our teams build digital products and platforms across industries including fintech, telecom, healthcare, SaaS, and AI-driven applications.

Can outsourced teams scale as the product grows?

Yes. Outsourced product teams can scale engineering capacity as product needs evolve, expanding or adjusting the team depending on development milestones and platform growth.

AI development services for product teams and technology leaders

Build AI-powered features, automation workflows, and data-driven systems with experienced engineers. From early prototypes to production-ready products, we help you apply AI where it creates real business value.

Talk to an AI engineer

Our clients

Trusted by startups and enterprises building complex digital platforms

Our expertise

Our teams have helped startups launch and scale new digital products

Over 220 projects

15+ years of experience

130+ people

90.6 client NPS for 2025

AI use cases

The role of AI in product development

Artificial intelligence is increasingly embedded into modern digital products and workflows, not as standalone systems, but as capabilities that automate processes, enhance user experiences, and improve decision-making.

Rather than building AI in isolation, most companies apply it in targeted areas where it delivers measurable value, from automating repetitive tasks to enabling smarter product features. The key challenge is identifying where AI is useful and integrating it into systems in a way that is reliable, scalable and aligned with business goals.

Automating repetitive workflows

AI is often used to automate multi-step workflows such as logging into platforms, retrieving data, processing documents, and triggering follow-up actions. This reduces manual effort and ensures processes run consistently at scale.

Enhancing product capabilities

AI enables product features such as intelligent search, content summarization, recommendations, and contextual insights. These features help users find information faster and interact more efficiently with the product.

Processing and structuring complex data

AI systems are used to extract and structure data from sources like invoices, reports, or large document sets. For example, transforming unstructured content into validated, usable data that can be stored, searched, or analyzed.

Supporting decision-making

AI can evaluate data and generate outputs such as classifications, scores, or recommendations that support human decisions. These systems are often designed with human oversight, especially in domains where accuracy and traceability are important.

what we build

Our AI engineering capabilities

We deliver AI development across the full range of capabilities product teams need to move from idea to deployed system. This includes agentic workflows, private infrastructure deployments for compliance-sensitive environments, and the model evaluation and fine-tuning work that determines whether a system performs reliably in production.

AI business case

For teams still evaluating where AI adds value: structured discovery sprints to identify high-impact use cases, followed by a quantified business case that frames the investment decision clearly before development begins.

Agentic AI systems

AI agents that perform multi-step tasks autonomously, navigating interfaces, retrieving documents, executing workflows, and interacting with external systems, with human oversight built in where accuracy requirements demand it.

Autonomous browser agents

AI agents that interact with web interfaces as a human operator would, logging in, navigating pages, extracting data, and completing form-based tasks, without requiring API access to the target system.

LLM integration and fine-tuning

Selection, evaluation, integration, and fine-tuning of large language models for specific tasks, including open-source models deployed in private infrastructure for teams with data residency or compliance constraints.

Workflow automation with AI

Multi-step business processes rebuilt around AI components: document ingestion, data extraction, structured output, scoring pipelines, and human-in-the-loop validation stages that improve accuracy over time.

RAG and knowledge retrieval

Retrieval-augmented generation (RAG) systems that allow large language models to answer questions grounded in a specific body of documentation or proprietary data. Relevant for any product where precision and source traceability matter.

Engagement model

How an AI engagement with Thinslices works

Most AI projects stall not because the technology is wrong but because the scope, the accuracy targets, and the economics were never clearly defined upfront. We structure engagements to resolve those questions before significant investment is made.

Start with a discovery sprint

A 4–6 week engagement focused on understanding your process, your data, and where AI is worth applying. The output is a clear use case, a technical direction, and a business case that makes the investment decision straightforward. Teams that already have a defined use case can move directly to a proof of concept.

Validate with a proof of concept

A time-boxed build against real data, with defined accuracy targets and benchmark results. The POC answers the questions that matter before full build commitment: which model, what accuracy is achievable, what it will cost per transaction, and what the human review workflow needs to look like.

Build & scale with a dedicated team

End-to-end product delivery in agile sprints, from MVP in a live environment through to scaled deployment. The same team that ran the POC continues into the build, carrying context and technical decisions forward. Human-in-the-loop feedback is built in from day one, so accuracy improves as the system processes real data.

Our clients

AI projects we have delivered

Across recent engagements in energy management, maritime operations, and academic publishing, the same pattern has appeared: a business process that repeats at scale, a need for high-accuracy automated output and constraints (regulatory, infrastructural, or operational) that rule out off-the-shelf tooling. Each project required a different technical approach, but all three started in the same place: a clear problem, a defined accuracy target, and a business case built before a line of code was written.

Energy management / B2B SaaS

AI invoice retrieval and data extraction for an energy management platform

An energy intelligence platform needed to automate end-to-end retrieval and extraction of utility invoices across hundreds of providers, replacing a manual analyst team and a third-party data service within a SOC 2-compliant security perimeter. We built a two-phase AI pipeline: an autonomous browser agent that logs into provider portals and retrieves invoices, followed by a fine-tuned LLM extraction layer that normalises data across provider formats. The entire stack runs on private infrastructure with no data leaving the security perimeter. A Model Context Protocol (MCP) server provides the extraction model with historical invoice context, enabling the accuracy gains needed to meet the production target. Currently achieving 97.1% data extraction accuracy against a 99% target. Business case projected savings of $2M over three years.

Maritime / Venture-backed startup

RAG-based compliance assistant and autonomous browser agent for maritime operations

A maritime technology venture needed to give vessel crews and shore teams fast, source-cited answers to compliance questions, and a way to aggregate live port intelligence from fragmented web sources without unified API access. We built a RAG-based agentic assistant that ingests vessel documentation, retrieves semantically relevant passages, and returns source-cited answers with confidence scores. Model responses are constrained to source material only, a hard requirement in a mission-critical context. A second phase added an autonomous browser agent that navigates port authority websites to surface live operational intelligence: weather, water depth, traffic restrictions. The platform is live, serving seafarers and shore teams across multiple languages.

Academic publishing / Enterprise

AI-assisted manuscript screening platform for a global academic publisher

A global academic publisher processing 30,000+ manuscript submissions per month needed to automate its pre-peer-review quality checks, replacing a fragmented manual process with a configurable workflow platform. We built a checklist engine modeled on fintech credit risk workflows: each check runs an LLM-powered assessment, produces a confidence score, and applies a provisional decision that a reviewer can confirm or override. Automation levels are configurable per journal. The platform consolidates all checks, documents, and decisions into a single workspace and automatically drafts rejection correspondence. Delivered in 5 months to MVP, with 6 automated checks live at launch.

Our technical stack

Technologies and platforms our teams work with

When building an MVP, the goal is to launch quickly without sacrificing the ability to scale the product later. Our engineering teams use modern frameworks, cloud platforms, and development practices that allow startups to move fast while building a solid technical foundation for future product growth.

We focus on technologies that support rapid iteration, reliable performance, and long-term maintainability.

Web & Frontend Development

Backend & Platform Engineering

Mobile Development

Cloud & Infrastructure

Automated Testing

AI & Data Platforms

1. How long does it take to build an AI proof of concept?

A technical proof of concept typically runs six to ten weeks, depending on data availability and task complexity. This includes model evaluation and benchmarking against real data, with defined accuracy targets and a signed-off technical approach as the output. Teams that have already completed discovery can move directly to this stage.

2. Can you build AI systems that keep data within our own infrastructure?

Yes. For clients with SOC 2, data residency, or other compliance requirements that prevent data from leaving a controlled environment, we have experience deploying open-source LLMs including Mistral and Qwen within private cloud or on-premises infrastructure. Credential handling, inference pipelines, and fine-tuning can all run within a client-controlled security perimeter.

3. What is a human-in-the-loop AI system?

A human-in-the-loop system is one where a human reviewer can inspect, confirm, or override AI-generated decisions before they are applied. This matters in high-stakes or regulated processes where full automation carries unacceptable error risk. We design human review interfaces, confidence scoring, and override workflows as core product features, not optional additions, and build in the feedback mechanisms that allow accuracy to improve over time.

4. How do you select and evaluate AI models for a specific use case?

Model selection is driven by the requirements of the task: accuracy targets, data volume, latency constraints, cost per inference, and deployment environment. We evaluate multiple models against client data through structured benchmarking before committing to a technical approach. Where fine-tuning is required, it is applied iteratively with defined accuracy milestones tracked throughout.

5. How much does it cost to build an AI product?

The cost depends on several factors: the complexity of the use case, the data available for training or fine-tuning, whether the system needs to run on private infrastructure, and the accuracy threshold required for production. A discovery sprint is the most reliable way to scope this accurately, as it produces a quantified business case and a technical approach before any significant build investment is made. For context, the projects on this page ranged from focused proof-of-concept builds to multi-phase products delivered over several months with dedicated teams.