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Solutions Built to
Ship & Scale
We don’t hand over a model and disappear. From first discovery call to live production system — and everything after — we own the outcome alongside you. Here’s exactly what we build and how we do it.
Trusted by leaders across every major sector
Six Solution Areas. One Partner.
Every solution is custom-built for your data, your workflows, and your outcomes — not a pre-packaged product wrapped in services.
RAG systems, fine-tuned LLMs, AI copilots, and document intelligence platforms — built on GPT-4, Claude, Llama, and open-source models. Enterprise-grade with full data governance.
- Custom RAG pipelines over your internal knowledge base
- Fine-tuned domain models for legal, medical, and financial language
- AI copilots embedded directly into your existing workflows
- Private deployment — your data never leaves your infrastructure
Predictive models, recommendation engines, demand forecasting, and anomaly detection — deployed to production with full MLOps infrastructure, not just notebooks.
- Churn prediction, fraud detection, credit scoring, demand forecasting
- Recommendation systems driving 40–60% of revenue
- Automated retraining pipelines — models improve over time
- Full model monitoring with drift detection and alerting
Visual inspection, object detection, medical imaging, and real-time video analytics — running at line speed on the edge or in the cloud, integrated with your existing cameras and systems.
- 99%+ defect detection on manufacturing production lines
- Medical image analysis for radiology, pathology, and dermatology
- Real-time object detection and tracking for logistics and retail
- Edge inference on NVIDIA Jetson — no cloud latency
Document intelligence, contract analysis, sentiment analysis, multilingual support, and conversational AI — transforming unstructured text into structured business value.
- Contract review extracting 150+ clause types with 99% accuracy
- Multilingual customer service AI handling 80%+ without humans
- Clinical NLP extracting structured data from EHR notes
- Regulatory document analysis and compliance automation
AI agents that plan, reason, and execute multi-step tasks autonomously — from research and drafting to API orchestration and process automation across your entire tech stack.
- Multi-agent workflows that handle end-to-end business processes
- Tool-using AI agents integrated with your CRM, ERP, and databases
- Autonomous document processing pipelines with human-in-the-loop
- Process automation reducing manual task load by 60–80%
The infrastructure that keeps AI systems alive and improving after launch — feature stores, model registries, CI/CD pipelines, drift monitoring, and the data pipelines that feed them.
- End-to-end MLOps platform on AWS, Azure, or GCP
- Automated retraining triggered by performance drift
- Data pipeline engineering from raw sources to feature store
- Model governance, audit logging, and compliance reporting
Not sure which solution is right? Our strategy consulting engagements start with the business problem — not the technology. We audit your data, map your processes, identify your highest-value AI opportunities, build the business case, and create a prioritised roadmap before a single line of code is written. Often the most valuable thing we do.
How We Manage Every Project
Seven phases. One team. Full ownership of the outcome. Click any phase to see exactly what we do, what we deliver, and how long it takes.
Discovery & Assessment
Before we write a line of code — or charge you a penny — we need to understand your world. Discovery is the phase most AI consultancies skip or rush. We don’t. The quality of everything that follows depends entirely on the quality of what we learn here.
We embed a senior consultant and a data engineer with your team for one to two weeks. We interview stakeholders, map current processes, audit existing data infrastructure, and pressure-test the business case for AI. We look for the highest-value opportunity — not the most technically interesting one.
- Stakeholder interviews across business, data, and technology teams
- Current-state process mapping — where does manual work slow things down?
- Data landscape audit — what exists, where it lives, quality and volume assessment
- Regulatory and compliance constraints identified upfront, not discovered later
- Business case pressure-test — is AI actually the right tool for this problem?
- Quick-win identification — what could be deployed in under 8 weeks for early ROI?
- Risk assessment across the 7 root causes of AI project failure
If Discovery concludes that AI is not the right investment for you right now, we tell you — clearly and in writing. We will never recommend a project that isn’t in your interest to build.
Strategy & Roadmap
Discovery tells us what’s possible. Strategy decides what to build and in what order. This phase produces the document that governs the entire engagement — the AI roadmap that your board, your CTO, and your team will use to make decisions for the next 12–24 months.
We define the precise problem statement for Phase 1, set measurable success criteria, design the change management approach, and produce a phased roadmap so the organisation can see the full journey — even if we’re only funding Phase 1 today. No surprises later.
- Single written problem statement agreed by all stakeholders — the north star
- Success criteria defined in business metrics, not technical metrics
- Phased roadmap: Phase 1 pilot, Phase 2 scale, Phase 3 expand
- Make-vs-buy decision with documented rationale for each component
- Change management strategy — who needs to change how, and when
- Executive sponsor identified and briefed with accountability framework
- Budget, timeline, and resource plan for Phase 1 confirmed
Data & Architecture
70% of AI project delays happen here — data that was assumed to exist doesn’t, or can’t be accessed, or is too low quality to train on. By running Data & Architecture in parallel with Strategy rather than after it, we compress the overall timeline by 3–6 weeks.
Our data engineers build the pipelines that move your data from its current home into a form that can train a model. Simultaneously, our solution architects design the full system — how the model will be served, monitored, retrained, and integrated with your existing tech stack. We design for production from day one, not as an afterthought.
- Data pipeline engineering — from raw sources to clean, labelled training sets
- Feature engineering — creating the predictive signals the model will learn from
- Data quality remediation — fixing labelling gaps, class imbalances, and coverage issues
- MLOps stack design — serving infrastructure, monitoring, feature store, model registry
- System integration design — APIs, webhooks, and data contracts with existing systems
- Security and data governance architecture — GDPR, HIPAA, SOC2 as required
- Environment setup — dev, staging, and production environments provisioned
Build & Train
This is the part everyone wants to start with. We don’t start here — and that’s why our models work. By the time we write the first training loop, we know exactly what problem we’re solving, what data we have, and how the model will live in production. The build is fast because the foundations are solid.
We run two-week sprints with weekly demos. You see working code every fortnight — not a black box that appears at week ten. Every model is benchmarked against your agreed success criteria before it proceeds to deployment. If it doesn’t meet the bar, we iterate until it does.
- Model selection and baseline — establishing the simplest model that meets the brief
- Iterative training with experiment tracking (MLflow) — every run logged and reproducible
- Hyperparameter optimisation and architecture search
- Evaluation against business KPIs — not just technical metrics like AUC or F1
- Bias and fairness testing for any model touching sensitive decisions
- Explainability layer (SHAP/LIME) for regulated industries
- Weekly demos to stakeholders — no surprises at go-live
Deploy & Integrate
Deployment is where most AI projects die. A model that runs in a Jupyter notebook is not the same thing as a model running in production — and the gap between the two requires serious engineering. We’ve bridged that gap 200+ times. It’s one of our core competencies.
We use blue/green deployment so there’s always a rollback option. We run shadow mode in parallel with your existing system — the AI makes decisions in parallel before it makes them for real, so you can validate outputs risk-free. Change management runs simultaneously: your team is trained, the process is redesigned, and adoption is measured from day one.
- Blue/green deployment — zero-downtime go-live with instant rollback capability
- Shadow mode validation — AI runs in parallel before taking live decisions
- Load testing at 10× expected production volume before go-live
- Integration testing across all connected systems and APIs
- User training sessions — every person who interacts with the AI is prepared
- Process redesign — workflows rebuilt around AI, not AI bolted on top
- Adoption dashboards live from day one — we track usage, not just performance
Monitor & Optimise
A deployed model is not a finished product. Models degrade. The world changes. Consumer behaviour shifts. New products are added. Fraudsters adapt. Every model in production needs active monitoring and periodic retraining to maintain its performance — and most organisations don’t build this capability before they need it.
We deploy monitoring from day one of production, not as an afterthought. Automated alerts fire when model performance drops below defined thresholds. Retraining is triggered automatically when data drift is detected. Monthly performance reviews give your team full visibility without requiring deep technical knowledge.
- 24/7 automated model performance monitoring with configurable alert thresholds
- Data drift detection — alerts when input data distribution shifts from training data
- Concept drift monitoring — alerts when real-world outcomes diverge from predictions
- Automated retraining pipeline triggered by drift or scheduled cadence
- A/B testing framework for comparing new model versions before full rollout
- Monthly performance review with business stakeholders — not just engineers
- Incident response SLA — P1 response within 1 hour, resolution within 4 hours
Support & Scale
The most successful AI programmes don’t stop at one model. Once Phase 1 proves ROI, the question becomes: what do we build next? How do we scale this to other regions, business units, or use cases? How do we build internal capability so we’re not dependent on external partners forever?
Our Support & Scale phase is designed to do two things simultaneously: keep your existing systems healthy and performing, and expand the AI programme according to the roadmap we built in Phase 2. Many clients move from a project engagement to a retained partnership — giving them a dedicated senior AI team without the cost of hiring one.
- Dedicated support team with named senior contacts — not an anonymous helpdesk
- Capability transfer programme — we train your internal team to own more over time
- New use case development following the same 7-phase methodology
- Platform expansion — scaling pilots to new geographies, business units, or products
- Annual AI programme review — are we building the right things in the right order?
- Access to Sabalynx research — early access to new techniques applicable to your stack
- Flexible retainer model — scale support up and down as the programme demands
Phase 01 Is Free.
Let’s Begin Discovery.
The Discovery & Assessment phase is free for all qualified projects. You’ll leave with a clear picture of your AI opportunities, a data audit, and an honest go/no-go recommendation — at no cost and no obligation.