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Kindlebit

AI Services

AI Services Built for Real-World Business

From AI strategy and Generative AI to intelligent agents, machine learning and computer vision, Kindlebit helps businesses turn AI opportunities into production-ready solutions.

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AI Consulting

We work through your processes, systems and data to decide what is worth automating, what should stay human, and what needs to change before AI is viable.

Workshops Architecture review Evaluation planning
Discuss AI Consulting

Business benefits

  • A ranked list of use cases with effort and value attached
  • Clear view of data gaps before spend is committed
  • A roadmap your board and engineering team both understand

Capabilities

  • Process and workflow mapping
  • Data and integration review
  • Cost, risk and build-versus-buy analysis
  • Delivery roadmap and sequencing

Example use cases

  • Choosing between three competing internal AI proposals
  • Building the business case for a first AI investment

AI Readiness Assessment

A short, structured assessment of whether your data, systems, security posture and team can support AI in production.

Data profiling Access review Scoring framework
Discuss AI Readiness Assessment

Business benefits

  • Know which use cases are viable now versus later
  • Identify data quality work before it derails delivery
  • Agree security and governance requirements up front

Capabilities

  • Data source and quality audit
  • Integration surface assessment
  • Security, residency and compliance review
  • Skills and operating model review

Example use cases

  • Pre-investment due diligence
  • Post-pilot decision on scaling

AI Product Development

End-to-end delivery of AI-native products, from concept and UX through to a released, monitored system.

React Node.js Python AWS Azure
Discuss AI Product Development

Business benefits

  • One team across AI, product, backend, frontend and cloud
  • Working software in weeks, not slide decks
  • Architecture that survives its first real users

Capabilities

  • Product discovery and UX
  • AI architecture and model selection
  • Full-stack build and QA
  • Release, monitoring and iteration

Example use cases

  • Launching an AI-first SaaS product
  • Adding a copilot to an existing platform

Generative AI Development

Applications built on large language models, grounded in your content and constrained to your business rules.

OpenAI Claude Gemini Llama
Discuss Generative AI Development

Business benefits

  • Draft, summarise and classify at volume with review in the loop
  • Consistent output quality measured by evaluation suites
  • Model choice kept swappable as the market moves

Capabilities

  • Prompt and context engineering
  • Structured output and function calling
  • Evaluation harnesses and regression tests
  • Cost and latency optimisation

Example use cases

  • Proposal and report drafting
  • Content classification pipelines

LLM Development

Domain-tuned language systems, including fine-tuning, adapters and open-weight deployments where privacy demands it.

Llama vLLM PyTorch Hugging Face
Discuss LLM Development

Business benefits

  • Behaviour tuned to your terminology and format
  • Option to run models inside your own tenancy
  • Predictable cost profile at scale

Capabilities

  • Dataset preparation and curation
  • Fine-tuning and adapter training
  • Self-hosted inference and scaling
  • Guardrails and output validation

Example use cases

  • Private assistants on confidential data
  • Highly structured domain outputs

RAG Development

Retrieval-augmented systems that answer from your documents and data, with citations back to the source.

Qdrant Pinecone PostgreSQL/pgvector LlamaIndex
Discuss RAG Development

Business benefits

  • Answers grounded in approved content, not model memory
  • New documents take effect without retraining
  • Every answer traceable to a source passage

Capabilities

  • Ingestion, parsing and chunking strategy
  • Hybrid and semantic retrieval
  • Reranking and citation
  • Freshness and permission-aware indexing

Example use cases

  • Internal knowledge assistants
  • Policy and compliance search

AI Agent Development

Agents that plan, call your tools and complete multi-step work — with permissions, logging and human escalation built in.

LangChain n8n Function calling Queues
Discuss AI Agent Development

Business benefits

  • Repetitive multi-system tasks completed without a person driving
  • Actions logged and reversible
  • Clear boundaries on what an agent may do

Capabilities

  • Tool and API integration
  • Planning and orchestration
  • Memory and state management
  • Human-in-the-loop checkpoints

Example use cases

  • Lead qualification and CRM updates
  • Operational reporting workflows

AI Chatbot Development

Assistants for web, app, voice and messaging channels that resolve enquiries and hand over cleanly when they can't.

Web SDK WhatsApp Business API Voice platforms
Discuss AI Chatbot Development

Business benefits

  • Round-the-clock first-line response
  • Consistent answers across channels
  • Escalation with full conversation context

Capabilities

  • Multi-channel deployment
  • Knowledge grounding
  • Live handover to agents
  • Conversation analytics

Example use cases

  • Customer support deflection
  • Product and pre-sales questions

Machine Learning

Custom models for forecasting, scoring, ranking and anomaly detection, trained on your own operational data.

Python scikit-learn XGBoost PyTorch
Discuss Machine Learning

Business benefits

  • Decisions based on patterns your reporting misses
  • Measurable accuracy against a baseline
  • Models retrained on a schedule that suits your data

Capabilities

  • Feature engineering
  • Model training and validation
  • Explainability
  • Batch and real-time scoring

Example use cases

  • Demand forecasting
  • Churn and risk scoring

Data Science

Analysis and experimentation that establishes whether the signal you need actually exists in your data.

Python SQL dbt BI tooling
Discuss Data Science

Business benefits

  • Evidence before engineering investment
  • Clarity on data quality and coverage
  • Findings communicated to non-technical stakeholders

Capabilities

  • Exploratory analysis
  • Statistical modelling
  • Experiment design
  • Dashboards and reporting

Example use cases

  • Pricing analysis
  • Customer segmentation

Computer Vision

Models that classify, detect and measure from images, video and live camera feeds.

PyTorch YOLO OpenCV ONNX
Discuss Computer Vision

Business benefits

  • Consistent inspection at volume
  • Automated measurement without manual review
  • Edge or cloud deployment depending on latency needs

Capabilities

  • Object detection and segmentation
  • Classification and OCR
  • Pose estimation
  • Video analytics pipelines

Example use cases

  • Defect detection on a production line
  • Movement analysis in training apps

Natural Language Processing

Classification, extraction and sentiment analysis over large volumes of text and conversation data.

spaCy Transformers Embeddings
Discuss Natural Language Processing

Business benefits

  • Structure from unstructured text
  • Automatic routing and prioritisation
  • Trends surfaced from customer feedback

Capabilities

  • Entity extraction
  • Intent and topic classification
  • Sentiment and tone analysis
  • Summarisation pipelines

Example use cases

  • Support ticket triage
  • Feedback and review analysis

AI Integration

Connecting AI capability into the systems your teams already use, rather than adding another separate tool.

REST GraphQL Webhooks Queues
Discuss AI Integration

Business benefits

  • AI appears inside existing workflows
  • No parallel process for staff to maintain
  • Data stays within governed systems

Capabilities

  • API and webhook integration
  • CRM, ERP and helpdesk connectors
  • Event-driven pipelines
  • Identity and permission mapping

Example use cases

  • Assistant inside your CRM
  • Automated enrichment on record creation

MLOps

The deployment, evaluation and monitoring layer that keeps AI systems reliable after launch.

Docker Kubernetes AWS Azure Observability tooling
Discuss MLOps

Business benefits

  • Regressions caught before users see them
  • Cost and latency tracked per feature
  • Repeatable releases instead of manual updates

Capabilities

  • CI/CD for models and prompts
  • Evaluation suites and golden datasets
  • Observability and alerting
  • Versioning and rollback

Example use cases

  • Scaling a pilot to production
  • Managing multiple models in one product

Not sure where to start?

Start With a Readiness Assessment

Two to four weeks to review your data, systems and processes, and return a ranked set of AI use cases with effort, risk and value attached.

Book an AI Consultation

Ready to put AI to work?

Let’s Find Your Highest-Value AI Opportunity

Whether you’re starting with an idea, exploring AI for your business or ready to scale an existing solution, our team can help you define the next step.

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