AI automation services
Prompt engineering, LLM integration, workflow orchestration and process automation. Smart systems that keep humans in control while handling the tedious work.
Trusted by forward-thinking teams
What we do
AI that actually fits your workflow
Little Tiger builds AI workflows that solve specific problems in your business. We design prompt engineering systems, integrate large language models into existing products, orchestrate multi-step AI workflows and automate processes that currently eat your team's time.
Our AI services are grounded in practical engineering: LLM integration with OpenAI and Claude, retrieval-augmented generation (RAG), structured output parsing, workflow orchestration and human-in-the-loop validation. Every system ships with guardrails, monitoring and cost controls. We delivered exactly this for Kindling's AI-powered data analysis and Anderson Publishing's automated editorial workflows.
Why invest in AI automation
AI without strategy is noise
Adding AI to every feature doesn't make a product better. Strategic automation targets the specific bottlenecks where it delivers measurable ROI and leaves everything else alone.
Manual processes hide opportunity
Every hour your team spends on repetitive classification, extraction or summarisation is an hour not spent on judgment calls that actually require human intelligence.
Your team's expertise is the moat
Generic AI tools are available to everyone. The competitive advantage comes from AI systems trained on your data, embedded in your workflows and shaped by your team's domain expertise.
Generic AI tools miss context
Off-the-shelf AI doesn't understand your business rules, your data schema or your compliance requirements. Custom AI integration embeds intelligence where it matters with the guardrails you need.
Automation should feel invisible
The best AI features don't announce themselves. They reduce friction, surface relevant information and handle tedious work in the background so your team barely notices the automation.
ROI requires measurement
AI projects without clear metrics become expensive experiments. Every system we build includes cost tracking, usage analytics and outcome measurement so you know exactly what the investment returns.
70%
Average reduction in manual processing time
95%+
Accuracy on domain-specific AI classification tasks
<500ms
Response times for production AI features
100%
Systems delivered with monitoring and cost controls
Prompt Engineering + LLM Integration
Production-grade LLM systems with structured, reliable outputs
We design prompt engineering systems that produce consistent, structured outputs from large language models (OpenAI GPT, Claude and open-source models). Structured output schemas, few-shot examples, chain-of-thought reasoning and retrieval-augmented generation (RAG) with your domain data.
Every LLM integration includes input validation, output parsing, fallback handling and cost monitoring. Built for production reliability.
Workflow Orchestration
Multi-step AI workflows that handle complexity gracefully
Complex tasks require more than a single API call. We build multi-step AI workflows using LangChain, custom orchestration layers and tool-use patterns. Models plan, execute sub-tasks, validate intermediate results and handle errors without human intervention.
Document processing pipelines, multi-source research workflows, content generation with review loops and data extraction with validation. All orchestrated with retry logic, state management and audit trails.
AI Strategy + Planning
Know where AI creates value before you invest
We help teams identify where AI delivers real ROI: auditing workflows, estimating automation potential and building a phased implementation plan. Not every process benefits from AI. We find the ones that do and prioritise by impact and feasibility.
Strategy engagements include workflow analysis, opportunity scoring, technology selection, cost modelling and a concrete implementation plan with targets and success metrics.
Process Automation
Eliminate repetitive work with targeted automation
We automate specific, well-defined processes: document classification, data extraction, content tagging, report generation, email triage and approval routing. Each automation is scoped tightly, tested against real data and deployed with monitoring.
We don't automate everything. We automate the repetitive, time-consuming tasks that free your team to focus on work that requires human judgment, creativity and relationship-building.
Responsible AI: guardrails, transparency and human oversight
Every AI system we build includes guardrails by default: input filtering, output validation, bias monitoring, cost controls and human-in-the-loop review for high-stakes decisions. We design for transparency. Your team can always understand why the system produced a given output.
AI works best when humans stay in control. We build systems that enhance capability and surface recommendations so your team makes the final call.
Selected work
AI + automation projects
Kindling Data Center
Applied AI / Conversational Analytics
2025
Kindling Data Center
Applied AI / Conversational Analytics
AI-powered conversational interface for cannabis e-commerce analytics. Natural language queries against POS data with contextual insights.
Anderson Publishing
Applied AI / Publishing Platform
2025
Anderson Publishing
Applied AI / Publishing Platform
AI-assisted editorial workflows for medical publishing. Automated content processing, JATS XML transformation and quality assurance.
Sundae
Applied AI / HR Software
2025
Sundae
Applied AI / HR Software
Intelligent scheduling automation integrating POS performance data, employee availability and demand forecasting for shift optimization.
How we work
Our approach to AI automation
Workflow audit + opportunity mapping
We observe your team's actual workflows, identify repetitive patterns and score each automation opportunity by impact, feasibility and risk. This audit determines where AI creates value and where it doesn't.
Prototype with real data
We build working prototypes against your actual data within the first two weeks. Your team evaluates real outputs, accuracy and usability before we invest in production engineering.
Production hardening
Prototypes that pass validation get production-grade engineering: error handling, retry logic, monitoring, cost controls, input validation and output guardrails. We build systems that work reliably at scale.
Human-in-the-loop design
For high-stakes outputs, we design review workflows where AI handles the heavy lifting and humans make the final call. Confidence scores, flagged exceptions and approval queues keep humans in control.
Monitoring + iteration
Post-launch, we monitor accuracy, latency, cost and usage patterns. Models get retrained, prompts get refined and workflows get adjusted based on real-world performance data.
Technologies we work with
Frequently asked questions
We primarily work with OpenAI (GPT-4o, GPT-4), Anthropic Claude and open-source models where appropriate. We recommend the model that best fits your accuracy requirements, latency constraints, cost budget and data privacy needs.
We design AI systems with data privacy as a first-class concern. Options include API providers with zero-retention policies, self-hosted models for sensitive data, data anonymisation pipelines and on-premise deployment where compliance requires it. Every project gets a data handling assessment.
Think of it as AI that handles the repetitive parts: surfacing recommendations, processing information at scale and managing routine tasks, while humans stay in the decision loop. Full automation removes human involvement entirely. We default to keeping people in the loop for any decision with real consequences.
We define success metrics before building: time saved, accuracy improvement, cost reduction, throughput increase or error rate reduction. Every system ships with usage analytics and outcome tracking so ROI is measurable from day one.
Yes. Most of our AI work integrates into existing applications by adding intelligent search, automated processing, content generation or recommendation features to products your team already uses. We design AI features that feel like they were always there.
We address hallucination through retrieval-augmented generation (grounding outputs in your data), structured output schemas, confidence scoring and validation layers. For high-stakes use cases, human review is always part of the workflow. We're transparent about what AI can and can't reliably do.
A focused AI feature (search, classification, extraction) takes 4–8 weeks. Multi-step workflow automation takes 8–12 weeks. Strategy engagements take 2–4 weeks. We prototype with real data in the first sprint so you see tangible results early.
Yes. AI systems need ongoing attention: model updates, prompt refinement, accuracy monitoring and cost management. We offer retainer-based support covering model maintenance, performance monitoring and continuous improvement.
Ready to put AI to work on your biggest bottleneck?
Tell us about the problem. We'll assess the opportunity, prototype a solution with your data and show you what's possible.
Services and Capabilities
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