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AI & Automation
AI solutions

Intelligent Automation.
Real Outcomes.

We design and deploy practical AI — not experiments. From agents that take action inside your systems to knowledge assistants and event-driven automations, we help your team move faster and do more with less.

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What We Build

  • AI Agents: task-performing systems that read your data and take real actions — update CRM records, create tickets, generate quotes, follow up leads, route exceptions, and notify teams. Designed with explicit scope boundaries and confirmation gates so they act with confidence where appropriate and escalate when they shouldn't proceed alone.
  • RAG Knowledge Assistants: secure, queryable access to your documents, SOPs, policies, and product catalogs — answers grounded in your actual data with source citations and role-based access controls. The difference between a knowledge assistant that works and one that confidently hallucinates is retrieval architecture, and that's where most implementations fail.
  • Workflow Automations: event-driven pipelines connecting your tools — email to CRM, form to ERP, webhook to Slack — with AI handling the classification, extraction, and decision points that rule-based automation can't. The volume your team handles manually today doesn't require more people; it requires the right automation in the right places.
  • Multi-channel Deployment: web chat, WhatsApp, email bots, internal Slack assistants, or embedded directly in your existing products and portals — meeting users where they already work rather than asking them to adopt a new tool to get answers.
  • Custom LLM Integration: model selection, prompt engineering, fine-tuning, and evaluation frameworks calibrated for your domain, terminology, and data — so the system performs well on your specific use case rather than on generic benchmarks that don't reflect your real workload.
  • Analytics & Monitoring: usage dashboards, accuracy tracking, latency monitoring, and continuous feedback loops — so you know how the system is performing in production, where it's failing, and what to improve next. AI systems without observability are systems you're flying blind on.

Common Triggers

  • "Our team is drowning in repetitive manual work": data entry, copy-paste between systems, routing emails, classifying tickets, filling forms — high-volume, low-complexity tasks that consume hours of skilled people's time every day. The work isn't intellectually demanding; it's just relentless. The people doing it are capable of far more valuable things, and they know it. The question isn't whether to automate it — it's finding the right way to do it without creating a fragile system that breaks every time something changes upstream.
  • "Customers ask the same questions and we can't respond fast enough": support queues growing, response times slipping from hours to days, and the same tier-1 questions — order status, return policy, account setup, pricing — answered a hundred times a day by people who could be handling complex escalations instead. Headcount is the obvious solution and the expensive one. AI deflection that actually works — that gives customers accurate, immediate answers without frustrating them into calling anyway — is the one worth building properly.
  • "We have years of documents and nobody can find anything": policies, SOPs, contracts, technical specs, onboarding guides, compliance documentation — institutional knowledge locked in files that nobody searches because keyword search never returns the right answer, so people ask a colleague instead, who may or may not know, who may or may not be available. A knowledge assistant that actually retrieves the right answer from the right document changes how a team operates — and how quickly new people become effective.
  • "We want to add AI to our product but don't know where to start": AI features on the roadmap, leadership asking for a demo, competitors shipping things that look impressive — but no internal experience with what LLMs, RAG, and agents actually take to build, evaluate, and ship reliably to production. The gap between a proof-of-concept that works in a notebook and a feature that works for real users under real conditions is where most product AI projects stall. We close that gap.

Use Cases by Function

  • Sales & Revenue: inbound lead capture and qualification routed to the right rep without manual triage, AI-drafted proposals and follow-up emails reviewed and sent by reps rather than written from scratch, CRM records auto-populated from call transcripts and email threads rather than entered manually after every interaction. The goal is a sales team that spends its time on conversations and relationships — not on the administrative layer that currently surrounds every deal.
  • Customer Support: tier-1 deflection for common, answerable queries — order status, return policy, account setup, product information — with smart escalation to the right human when the query is genuinely complex, along with full conversation context so the agent doesn't ask the customer to repeat themselves. Multi-language support and 24/7 availability without proportional headcount — and deflection rates that actually hold up because the answers are grounded in your real data, not generic responses that frustrate customers into calling anyway.
  • Operations & Back Office: structured data extraction from unstructured documents — invoices, purchase orders, delivery notes, contracts — processed at volume without manual keying, with validation logic that flags exceptions for human review rather than passing errors downstream. Compliance form processing, inventory exception alerts triggered by real data rather than end-of-day reports, and automated reporting that pulls from live sources so the numbers are current when decisions need to be made.
  • Internal Productivity: company knowledge bases your team can query in plain language and actually get the right answer from — policies, SOPs, technical documentation, compliance guidelines — rather than the folder-and-keyword search that sends people asking colleagues instead. HR policy assistants, IT helpdesk first-response bots, structured onboarding that gives new hires access to institutional knowledge from day one, and meeting summarisation that produces actionable outputs rather than a transcript nobody reads.

How We Work

01

Discovery

Audit your workflows and data sources. Identify high-ROI automation candidates and define success metrics.

02

Design

Define agent scope, data flows, integration points, and safety guardrails before a line of code is written.

03

Build & Evaluate

Iterate with your team, evaluate outputs against real data, and tune prompts and retrieval pipelines.

04

Deploy & Improve

Production rollout with full logging, performance monitoring, and a roadmap for continuous improvement.

Models & Tools We Work With

  • LLMs: Claude (Anthropic), GPT-4o (OpenAI), Gemini (Google) — model selection based on your cost, latency, accuracy, and data residency requirements, not brand preference. Different models have meaningfully different characteristics for reasoning depth, context window, output consistency, and per-token cost — choosing the right one for the task type can halve your inference cost or substantially improve output quality, and the right choice for your customer-facing assistant may be different from the right choice for your internal data extraction pipeline.
  • RAG & vector search: Pinecone, pgvector, Weaviate, Chroma — retrieval pipelines designed for your document structure, query patterns, and freshness requirements. Poor retrieval is the most common cause of RAG hallucination — the model can only answer correctly if it retrieves the right context first, and retrieval quality is determined by chunking strategy, embedding model choice, metadata filtering, and re-ranking, all of which need to be calibrated for your specific document corpus rather than left at defaults.
  • Agent frameworks: LangChain, LlamaIndex, custom agent loops — the right level of abstraction for your use case without unnecessary framework lock-in. Heavyweight frameworks add abstraction layers that make debugging difficult and failure modes opaque; custom agent loops require more upfront engineering but give precise control over retry logic, tool calling behaviour, and error handling — the choice is made based on your complexity and reliability requirements, not which framework has more GitHub stars this month.
  • Automation platforms: n8n, Make, Zapier, or custom event-driven pipelines — depending on whether your automation needs low-code speed or production-grade reliability. Low-code platforms are the right choice for straightforward integrations with moderate volume and low failure-cost; custom pipelines are the right choice when you need dead-letter queues, retry logic, schema validation, monitoring, and the ability to handle failure gracefully rather than silently dropping events.
  • Embedding & fine-tuning: OpenAI Embeddings, Cohere, custom fine-tuning pipelines for domain-specific terminology and classification tasks. Fine-tuning is often oversold — most use cases are better served by better retrieval architecture, improved prompting, and richer context than by fine-tuning a model on domain data, which is expensive, requires ongoing maintenance, and doesn't compose well with retrieval. We use fine-tuning for the specific scenarios where it genuinely outperforms the alternatives: narrow classification tasks, highly domain-specific vocabulary, and output format consistency at scale.
  • Deployment & observability: LangSmith, Helicone, Datadog, and custom logging — so you know what your AI is doing in production, when it fails, and why. Without structured observability, AI systems in production are effectively black boxes — you can't diagnose unexpected outputs, can't demonstrate to auditors what the system did and why, can't identify which query types are underperforming, and can't make data-driven decisions about where to invest in improvement versus where the system is already working well.

Governance & Security

  • Access controls: role-based permissions and data boundary enforcement built into the retrieval and action layers — users only see what they're authorised to see, and agents only act within the scope they've been explicitly granted. An AI system with access to everything everyone is allowed to see is a different kind of risk than a human with the same access, because it operates at scale and without the intuition that flags something as wrong before acting on it.
  • Auditability: full prompt and response logging, version tracking, and audit trails that let you reconstruct exactly what the system did, with what input, and what it returned — essential for debugging unexpected outputs, demonstrating compliance to auditors, and understanding how a decision or action was reached when someone asks.
  • Safe integrations: non-destructive integration patterns with explicit confirmation gates before irreversible actions — agents don't send emails, delete records, or overwrite data without an explicit check. The right level of autonomy for each action type, not a single setting that either does too much or too little.
  • Model behaviour controls: system-level guardrails, output filtering, hallucination mitigation strategies, and graceful fallback handling — so the system responds to uncertainty with "I don't know, here's who to ask" rather than confidently generating a plausible-sounding answer that happens to be wrong.
  • Vendor independence: architectures that treat the LLM provider as a replaceable dependency rather than a foundation — so when a better model ships, when pricing changes, or when your data residency requirements shift, you can swap providers without rebuilding the system around it.
  • Privacy-aware by design: GDPR and data residency requirements built into the architecture from the start, not addressed as an afterthought when a customer asks. Your data is never used to train third-party models without explicit consent. What your users put into the system stays in the system — and in the jurisdiction it's supposed to stay in.

Who We Work With

  • Operations-heavy businesses: logistics, distribution, field services, and manufacturing teams processing high volumes of documents, orders, and exceptions every day — where a meaningful percentage of staff time goes into work that follows consistent patterns and could be handled by a well-designed automation, freeing the team for the judgement calls that actually require a person.
  • Customer-facing businesses with support scale problems: ecommerce, financial services, and SaaS companies whose support volume has grown past what their team can absorb — response times slipping, queues lengthening, tier-1 questions consuming capacity that should go to complex cases. The team isn't inefficient; the volume has simply outpaced what headcount alone can solve.
  • Knowledge-intensive organisations: legal, compliance, consulting, and professional services firms sitting on years of valuable documents — case notes, precedents, policies, research, contracts — that are structurally impossible to search with keyword tools and practically impossible to keep in anyone's head. The knowledge exists; the problem is retrieval, and that's exactly what RAG is built for.
  • Product teams adding AI to their SaaS: companies building AI-powered features into an existing product — summarisation, recommendations, assistants, intelligent search — without the internal experience to evaluate models, design reliable retrieval pipelines, handle edge cases gracefully, and ship features that actually work for real users at scale rather than in controlled demos.

Why Kubrik for AI

  • We build for production, not for demos: anyone can wire up an LLM and make something impressive in a meeting. The hard part is reliability, error handling, edge case behaviour, graceful degradation, and a system that holds up when real users push it in directions you didn't anticipate. That's where most AI projects fail — and it's where we focus. Every system we build is designed to behave predictably in production, not just in controlled evaluation conditions.
  • We start with the problem, not the technology: AI isn't the right answer to every question. We'll tell you when a simpler automation, a SQL query, or a rule-based system is faster, cheaper, and more reliable than a language model — and we'll save you months of complexity in exchange. When AI is genuinely the right tool, we know how to implement it properly. When it isn't, we won't sell you one.
  • We integrate, not replace: the most effective AI implementations work alongside your existing tools, data, and workflows — not as a standalone system your team has to context-switch into. We build for deep integration with your CRM, ERP, ticketing systems, and communication channels so the AI is embedded in how your team already works, not another tab to check.
  • We stay accountable after launch: AI systems don't stay static. Model providers update, data drifts, usage patterns shift, and the edge cases multiply as adoption grows. We build in monitoring, feedback loops, and improvement processes from day one — and remain available to tune, extend, and respond as the system encounters the real world over time.

Results You Can Measure

Significant reductions in time spent on repetitive manual tasks — hours recovered per week per team member, not marginal percentage improvements. Faster customer response times and higher deflection rates without proportional headcount growth. Better data quality in downstream systems when AI handles extraction, classification, and routing consistently instead of people doing it manually and inconsistently. And AI features that actually ship to production and get used — not proof-of-concepts that impress in a meeting and quietly disappear once the implementation reality sets in.

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