AI consulting & roadmap
Use-case discovery, feasibility, data assessment and a prioritised plan with baselines.
Plan the roadmapAI that survives contact with your data.Artificial intelligence services.
Artificial intelligence services.
Practical AI, measured and governed.
AI projects succeed when they start with a decision worth improving and data you can trust. Surfytech scopes small, measurable use cases, builds them with human oversight, and monitors them once they are live.
- Use-case first
- Human oversight
- Governed & monitored
Why AI projects stallThe model is rarely
The model is rarely
the hard part.
Most AI initiatives struggle with data quality, unclear decisions to improve, or nobody owning the outcome after a demo. A prototype on sample data proves very little about production.
We start from the decision, measure the baseline, then build only what improves it — with evaluation, oversight and monitoring planned before the first model trains.
Automate, assist or predict? Plenty of valuable work is a rule or a better screen, not a model. We compare each option on value, risk and effort before recommending one.
- 01A measured starting point
Baseline performance agreed first, so improvement can be proven.
- 02Data you can defend
Sources, labels and gaps documented before models depend on them.
- 03Oversight where it matters
Human review for consequential decisions, with a clear escalation path.
- 04Operations after launch
Monitoring, drift alerts and an owner for model behaviour.
What we deliverFocused AI services
Focused AI services
tied to business outcomes.
Each service can be delivered alone or as part of a wider data and automation programme.
AI-powered automation
Document handling, routing, matching and exception workflows with human review.
Discuss automationAI-driven data analytics
Forecasting, scoring and anomaly detection fed by governed, reconciled data.
Explore analyticsNatural language processing
Classification, extraction and summarization for documents, messages and tickets.
Discuss NLPAI chatbot development
Assistants grounded in your own content with hand-off, guardrails and escalation.
Discuss assistantsLet’s decide together.
Share the decision, the data and what a wrong answer would cost. We’ll recommend the lightest approach that works.
Talk through your use caseAI capabilities
Every layer. One accountable team.
Data, models, integration and governance planned together — because AI systems fail at the seams otherwise.
Use-case discovery
Problem framing, baseline measurement, feasibility and risk assessment per use case.
Model development
Feature design, training, evaluation and selection of appropriate methods and models.
Documents & vision
Extraction from documents, forms and images with confidence thresholds and review queues.
Conversational AI
Grounded assistants with retrieval, guardrails, analytics and human escalation.
MLOps & monitoring
Versioning, retraining triggers, drift detection, performance reporting and rollback.
Ethics & governance
Fairness reviews, documented decisions, impact assessment and model inventories.
Security & privacy
Access control, data minimisation, audit trails and careful handling of sensitive inputs.
Integration
Models served into your apps, workflows and BI tools through monitored APIs.
AI depends on solid data and integration. Explore data analytics, system integration and machine learning services.
Our AI processProve it small.
Prove it small.
Then earn the right to scale.
Discovery → Data assessment → Prototype → Build & integrate → Validate & govern → Operate. Each phase ends with evidence, not enthusiasm.
Discovery
Frame the decision, the baseline and the cost of being wrong for each candidate use case.
Output: use-case shortlist & baselineData assessment
Check sources, labels, quality, access and gaps. Decide what is feasible today.
Output: data readiness reportPrototype
Build a focused pilot and measure it against the agreed baseline.
Output: pilot & measured resultsBuild & integrate
Harden the pipeline, serve the model and wire it into real workflows.
Output: production-ready serviceValidate & govern
Evaluate for accuracy, fairness, security and failure modes with stakeholder review.
Output: evaluation & governance recordsOperate
Monitor drift and performance, manage retraining and iterate with feedback.
Output: monitoring & improvement loop
Platforms & toolingTooling chosen for
Tooling chosen for
repeatability, not hype.
We work with mainstream frameworks and evaluation tooling so experiments remain reproducible and production behaviour stays explainable.
MLflowExperiment & model tracking
TensorBoardTraining visibility
FairlearnFairness evaluation
DataRobotAutomated modelling options
Watson AnalyticsEnterprise analytics services
PostmanAPI testing & documentation
ApplitoolsVisual validation for AI UIs
MablAutomated test flows
FunctionizeContinuous functional testing
Models are only one option. We compare rules, classical analytics and existing platform features before recommending new model development.
Sectors we work withAI shaped by
AI shaped by
how each sector decides.
What counts as a wrong answer — and what it costs — differs by industry. We design oversight around that reality.
Healthcare
Document extraction and operational forecasting with clinician review in the loop.
Healthcare technologyFinance
Scoring, anomaly detection and monitoring with auditable decisions and limits.
Finance solutionsManufacturing
Quality signals, demand forecasting and downtime prediction on plant data.
Manufacturing solutionsRetail
Recommendations, pricing signals and stock forecasting across channels.
Retail solutionsEducation
Progress signals and content assistance with educator oversight.
Education solutionsTravel
Demand forecasting, recommendations and service automation for travel operators.
Travel solutionsCase studies & solution examplesSee the problem.
See the problem.
Imagine the possibilities.
The scenarios below illustrate possible AI outcomes—not published client case studies or measured results. Ask us about relevant experience for your data.
Illustrative · Documents
Invoices keyed by hand
Challenge: Staff transcribe line items into the finance system, so speed and accuracy vary by person.
Approach: Extraction with confidence thresholds and an exception queue for human review.
Discuss document AI →Illustrative · Forecasting
Stock ordered on gut feel
Challenge: Seasonality and promotions drive stockouts in some lines and markdowns in others.
Approach: A validated forecast with assumptions, accuracy tracking and a review cadence.
Discuss forecasting →Illustrative · Support
Tickets answered with copy-paste
Challenge: Agents search for answers while customers wait for a first response.
Approach: An assistant grounded in approved content with hand-off and escalation rules.
Discuss assistants →Frequently asked questionsGood questions.
Good questions.
Clear starting points.
Here’s what to consider before starting an AI initiative.
Ask about your use case →What artificial intelligence services does Surfytech offer?
Surfytech provides AI consulting, use-case discovery, machine learning development, document and language processing, conversational AI, analytics, governance and integration into existing business systems.
How do we know if AI is the right approach?
We start from the decision you want to improve, measure the current baseline, and compare rules, analytics and model approaches on value, risk and effort. Sometimes the best answer is a simpler change.
Do you use our data to train public models?
Data handling is agreed before work starts. We do not send your data to third-party training pipelines without explicit agreement, and we document retention, access and deletion expectations.
How accurate will the model be?
We report evaluation results on held-out and representative data with expected error ranges, and we avoid promising precision your data cannot support. Performance is re-measured as conditions change.
How long does an AI pilot take?
A focused pilot typically runs for weeks rather than quarters, because scope is deliberately narrow: one decision, one data path and one measurable baseline agreed at the start.
Do you support the system after launch?
Yes. Support covers monitoring, drift review, retraining triggers, fixes and enhancement work. Coverage, response expectations and cost are agreed before launch.
Start your AI projectLet’s find the use case
Let’s find the use case
worth automating first.
Tell us the decision you want to improve, the data behind it and how errors would be handled. We’ll propose a scoped pilot with a measurable outcome.
No complete specification needed. A clear business goal is a great place to start.
What happens next?
- Share the decision, data sources and the current baseline.
- Discuss feasibility, oversight, integration and cost.
- Agree a scoped pilot and the scope for a proposal.