We build custom AI and machine learning solutions that eliminate repetitive work, reduce errors, and let your people focus on decisions—not data entry. From document processing to intelligent classification, we turn your operational bottlenecks into automated workflows that scale.
Custom AI/ML
AI that solves problems.
Not AI for its own sake
After a dozen AI solutions launched for leading US and European companies, mostly from finance and professional services industries, we've learned what works—and what doesn't. The difference between a successful AI project and a failed one rarely comes down to the technology. It comes down to whether the problem was right for AI in the first place.
That's why we start every engagement with rigorous validation. We assess feasibility, define success metrics, and build a working prototype before committing to full development. You see results before you commit resources—and if AI isn't the right answer, we'll tell you before you've spent a cent on development.
Our goal is to deliver measurable improvement to your operations—faster processing, fewer errors, lower costs, better decisions. Here are some examples of actual results we were able to achieve:
AI Impact at Scale
- 3x
Faster equity valuation
Achieved through AI integration into each time-consuming process, starting from data extraction from client documents, ending with an AI-driven peer selection and automated market research.
- 45x
Faster data extraction
Delivered by an AI-powered system that automates collection, categorisation, and extraction of regulatory documents at scale—eliminating manual monitoring and data entry.
- 35%
Faster new hire onboarding
Enabled by an AI-driven knowledge management assistant that reduces time spent on in-person explanations and eliminates delays from self-guided learning failures.
- 60%
Faster regulatory processing
Delivered by an AI-powered system that automates collection, categorisation, and extraction of regulatory documents at scale—eliminating manual monitoring and data entry.
Proven solutions to the challenges we're asked to solve most often
Intelligent Document Processing Platform
The solution extracts structured data from PDFs, scanned documents, and unstructured files—handling exceptions and edge cases that rule-based automation cannot address. It reads, interprets, and transforms documents into actionable data at scale.
Automated Classification & Categorisation System
The solution automatically sorts, tags, and routes items using custom taxonomies aligned with your business logic. Whether classifying companies, transactions, support tickets, or content, the system trains on your categories—not generic industry models.
Regulatory & Compliance Processing Platform
The solution processes regulatory filings, compliance documents, and legal texts at scale. It extracts required data points, flags anomalies, and maintains comprehensive audit trails—eliminating the need for manual document review.
Anomaly & Exception Detection System
The solution identifies outliers, fraud indicators, and quality issues in your data before they escalate. Detection models are tuned to your specific domain, risk tolerance, and operational thresholds.
Enterprise Knowledge Search Platform
The solution transforms unstructured information—documents, emails, transcripts—into searchable, queryable knowledge. It enables teams to surface answers in minutes rather than hours, reducing dependency on institutional memory.
Industries where we support
organisations with mission-critical
digital systems
Our custom AI development services boost productivity and workflows. Clients trust us for AI solutions that bring insights, reduce errors, and streamline workflows, while staying compliant and secure.
We partnered with one of the largest multi-billion AUM asset management companies to build a cutting-edge self-service investor toolkit—a rewarding deep dive into modern investment practices and client empowerment.
We built an intelligent SEC filings extraction system for a multi-billion AUM asset management firm—automating the capture of critical data points from 10-K, 10-Q, and 8-K filings at scale within hours of publication.
We developed an AI-powered regulatory processing platform for a global payment solutions provider—streamlining compliance workflows across multiple jurisdictions and reducing manual document review to minutes.
We delivered an AI-driven company classification engine for a Big Four professional services firm—enabling consistent, high-accuracy categorisation across custom taxonomies and millions of entity records.
Case
HecateCustomAI/MLRetail
AI-based sentiment collection CRM platform
We created Hecate Mapbox as a AI-based sentiment-enriching CRM to help small and medium businesses gain deeper insights from their customer data. The system collects, organizes, and visualizes data, turning it into smart, interactive map overlays that enhance decision-making and improve customer engagement.
Not sure which applies to you? That's fine - we start every engagement by understanding your problem.
How we work
From Challenge to Production:
Shipping Solutions Built for the Business’ Specific Needs
Every AI/ML engagement is unique. Company data, workflows, edge cases, even the definition of success—none of it matches a template. Building a solution that truly fits requires commitment from both sides: your team's domain expertise and access to real scenarios, and our technical depth and delivery discipline. We don't pretend otherwise.
What we do promise is to make the process as efficient and predictable as possible, with clear milestones, honest communication, and no wasted cycles.
Discovery & Validation
Discovery begins with a series of focused meetings designed to build a deep understanding of a challenge. We map current workflows, interview subject-matter experts who live the problem daily, and align with leadership on the desired future state. This phase is about understanding context, constraints, and what success actually looks like.
Most discovery work happens on-site, where we can observe processes firsthand and build rapport with your team. However, we're fully equipped to conduct these sessions remotely when logistics or preferences require it. By the end of discovery, both sides have a shared understanding of the problem and a clear path forward.
Proof of Concept
Before building a complete solution, we develop a focused proof of concept targeting a specific pain point or workflow segment that can be reasonably achieved within 8 weeks. The goal is to validate that there is chemistry between teams and that the technical approach works in real-world conditions—data, other systems, and the environment.
Throughout the PoC phase, you have full transparency into team composition and progress. We establish regular review cycles so you never have to wonder where things stand. Depending on your data sensitivity requirements, we can work with anonymised sample data or connect directly to production systems. The PoC concludes with measurable results and a clear go/no-go decision point.
Production
Development
Once the approach is validated, we move into production development, focusing on building a reliable, scalable, and integrated solution with the existing systems. Work proceeds through continuous iterations—each cycle delivers incremental functionality, incorporates feedback, and refines the solution based on discoveries. Planning is ongoing, not a one-time event. Priorities and scope adjust as the project evolves and new insights emerge.
We maintain close collaboration throughout, with frequent checkpoints to demonstrate and review completed work, discuss upcoming priorities, and address any blockers. The result is a production-grade system built to handle real-world volume and complexity.
User Testing &
Feedback Collection
Before considering the solution complete, we conduct structured user testing to validate that what we've built performs as expected in real conditions. This phase focuses on measuring actual outcomes against the metrics defined during discovery—processing time, accuracy rates, error reduction, user adoption, and any other KPIs that matter to your business. The feedback is collected from end users to identify usability issues and areas for refinement.
The data collected during this phase informs final adjustments and establishes a baseline for ongoing performance monitoring. If results fall short of targets, iterations continue until targets are met.
Not sure which applies to you? That's fine - we start every engagement by understanding your problem.
Engagement Models
Team Retainer
The primary engagement model for ongoing AI/ML development and support. Guarantees dedicated team capacity, predictable budgeting, and priority scheduling. Works best for continuous development, iterative improvements, and long-term partnerships where team familiarity with your systems drives efficiency.
Fixed Price (Proof of Concept)
Available exclusively for PoC engagements with a clearly defined scope and success criteria. Provides cost certainty during validation—you know exactly what you're investing before we begin. Concludes with documented findings, performance metrics, and actionable recommendations.
Time & Materials (Project Boost)
Best suited for short-term acceleration, temporary capacity expansion, or specialised expertise. The billing happens regularly, based on actual hours spent, supported with full transparency into team composition and time logged. Maximum flexibility to scale up or down as project needs evolve.
Professional Outstaffing
Dedicated AI/ML professionals embedded within your organisation, reporting to your management and following your processes. Ideal when there is a need for a long-term capacity with direct oversight or compliance requirements. We handle recruitment and employment—you get talent.
Case
HecateCustomAI/MLRetail
AI-based sentiment collection CRM platform
We created Hecate Mapbox as a AI-based sentiment-enriching CRM to help small and medium businesses gain deeper insights from their customer data. The system collects, organizes, and visualizes data, turning it into smart, interactive map overlays that enhance decision-making and improve customer engagement.
Frequently Asked
Questions
How do I know if my problem is suitable for AI/ML?
Generally, AI works well when you have a repetitive task that follows patterns, sufficient historical data to learn from, and a clear definition of what "correct" looks like. We have a custom framework on how to predict the relevance of AI impact. During discovery, we assess these factors and give you an honest recommendation. If your problem isn't a good fit for AI, we'll tell you—and suggest alternatives that might work better.
What data do you need to get started?
For discovery, we need access to representative samples of the data you're working with—documents, records, transactions, or whatever is relevant to your challenge. It doesn't need to be clean or perfectly organised. During the PoC phase, we can work with anonymised or dummy data if sensitivity is a concern, though production data typically yields more accurate validation.
How long does a typical project take?
A proof of concept typically takes 6–8 weeks. Production development varies based on complexity, integration requirements, and scope—most projects range from 3 to 6 months. We establish realistic timelines during discovery and provide regular progress updates throughout.
What if the proof of concept doesn't deliver the expected results?
That's exactly why we start with a PoC—to validate the approach before committing significant resources. If results fall short, you've invested 8 weeks instead of 8 months. We'll share what we learned, explain why it didn't work, and recommend whether a different approach might succeed or whether AI isn't the right solution for this problem.
How do you handle sensitive or regulated data?
We have extensive experience working with financial services and professional services firms where data sensitivity is paramount. We can work on-premise, within your cloud environment, or with anonymised datasets depending on your requirements. All engagements include appropriate NDAs and data handling agreements.
What's the typical investment for a project like this?
A proof of concept typically ranges from £25 000 to £50 000, depending on complexity and data requirements. Production development costs vary significantly based on scope, integration depth, model complexity, and infrastructure requirements—most projects fall between £50 000 and £250 000. Enterprise-scale deployments with multiple integrations, custom model training, and high-availability requirements may exceed this range. Our retainer and time-and-materials engagement models ensure that you pay for actual work delivered, with full transparency into team composition, hourly rates, and hours logged. We provide detailed estimates after discovery so there are no surprises.
Do we need in-house AI expertise to work with you?
No. We handle the technical implementation end-to-end. What we do need is access to your subject matter experts—the people who understand your processes, data, and what good outcomes look like. Their domain knowledge is essential for building a solution that actually fits your business.
Can you integrate with our existing systems?
Yes. We're a software development company first, which means we build solutions designed to integrate with your existing infrastructure—ERPs, CRMs, document management systems, databases, APIs. During discovery, we map out integration requirements and factor them into the project plan.
Who owns the intellectual property?
You do. The models we train on your data, the code we write for your solution, and any custom components we develop are yours. We retain no rights to your data or the resulting system.
What happens after the solution is deployed?
AI systems benefit from ongoing monitoring and refinement. We offer retainer arrangements for continued support—monitoring performance, retraining models as your data evolves, and making improvements based on user feedback. Alternatively, we can train your team to maintain the system independently and provide support on an as-needed basis.