We don't sell AI. We build the specific, measurable capability your organisation needs — then we stay until it performs. Every engagement starts with a 30-minute discovery call and ends with a documented outcome.
The average knowledge worker spends 41% of their working day on tasks that AI could handle — drafting, summarising, searching, formatting, routing, and chasing approvals. We identify exactly where that time is going, build the AI tooling to reclaim it, and measure the hours recovered.
This isn't generic software. We custom-engineer productivity systems around your workflows, your tools, and your people — integrating LLM capabilities directly into the environments your teams already use.
Book a productivity auditLLM integrations inside Slack, Teams, email, and CRM — surfacing summaries, drafting responses, and routing decisions without leaving the tool. We connect to your existing stack via API, not another dashboard.
AI that reads, extracts, classifies, and routes documents at scale. Contracts, invoices, reports, compliance filings — processed at machine speed with human-defined rules and edge-case escalation paths.
Automated transcription, action extraction, and knowledge base population from every meeting, call, and discussion. Your institutional knowledge stops living in inboxes and starts working for you.
Most mid-market organisations are sitting on years of operational data that has never been properly analysed. We build the infrastructure to transform that data into forward-looking decisions — using predictive modelling, anomaly detection, and dashboards designed for how your leadership team actually thinks.
The global predictive analytics market hit $22.2 billion in 2025 and is growing at 19.8% annually. The organisations that build the capability now will have an advantage their competitors can't easily close.
Request a data strategy sessionDemand forecasting, churn prediction, revenue projection, and risk scoring — built on your historical data and recalibrated as new data arrives. We use gradient boosting, neural networks, and hybrid approaches depending on your signal quality.
Systems that watch your data streams continuously and surface exceptions before they become incidents. Pipeline failures, fraud signals, SLA breaches, quality drift — detected at the point of emergence, not in the Monday morning report.
Dashboards built around decisions, not metrics. We interview your leadership team to understand how they think, what they need to act on, and what noise they want filtered out — then we build accordingly.
λ kairos analytics --connect --source "your_data_warehouse" ✓ Data ingestion Batch + streaming. Kafka, S3, Snowflake, BigQuery supported. ✓ Feature engineering Automated with human review checkpoints. ✓ Model selection Ensemble methods. XGBoost + LightGBM + custom NN where needed. ✓ Anomaly watch LSTM + isolation forest. Threshold calibrated to your tolerance. ✓ Dashboard deploy Grafana / Metabase / Superset or embedded in your existing BI. Stack: Python · dbt · Airflow · MLflow · PostgreSQL · Redis Cloud: AWS · GCP · Azure — or on-prem if required λ
Standard RPA breaks the moment the UI changes. We build intelligent process automation — systems that understand intent, handle exceptions, adapt to variation, and improve continuously through feedback loops. Before any automation is built, we run a process mining analysis to identify which processes actually deserve to be automated.
Organisations deploying intelligent automation report 40–60% reduction in manual data entry, 25–35% improvement in response times, and payback within 3–6 months of deployment.
Get a process auditBefore we automate anything, we map exactly how your processes actually run — not how they're supposed to run. Using event log analysis and process discovery tools, we surface bottlenecks, rework loops, and automation-ready handoffs that would otherwise take months to find manually.
RPA bots enhanced with LLM reasoning for exception handling, unstructured data processing, and adaptive decision-making. Unlike rule-based RPA, our systems handle variance gracefully — they don't break when the invoice format changes or the form field moves.
Multi-step autonomous workflows that span systems, make conditional decisions, and loop back on their own outputs. Built on modern agentic frameworks — LangChain, AutoGen, custom tool-use architectures — designed for production reliability, not demo performance.
The best specialists in RLHF engineering, RISC-V design, post-quantum cryptography, and silicon photonics are not on job boards. They are heads-down on projects, invisible to standard hiring tools. We build and operate deep-web candidate intelligence systems that surface passive talent across GitHub, arXiv, conference proceedings, patent databases, and niche technical communities.
The AI recruitment market hit $704M in 2025. Hiring cycles are shrinking for organisations using AI, while manual processes lengthen as the candidate pool for niche roles tightens. The window to build this capability is now.
Discuss a hiring briefAI-powered crawling and signal extraction across GitHub contribution graphs, arXiv publications, IEEE proceedings, patent filings, and Stack Overflow expertise clusters. We find the person who built the thing you need — before they're looking for a job.
Multi-dimensional candidate scoring against your specific technical and cultural brief — not keyword matching. Our models evaluate demonstrated capability, project complexity, contribution quality, and career trajectory to surface candidates who are genuinely exceptional, not just searchable.
Predictive modelling of candidate conversion rates, offer acceptance probability, and retention risk. Your hiring team knows which candidates to prioritise, when to move, and what offer will close — before the final round.
λ kairos recruit --role "RLHF Engineer" --tier "senior" --passive true Scanning deep-web sources... ✓ GitHub RLHF repositories with 50+ stars, active committers extracted. ✓ arXiv Published authors: alignment, RLHF, Constitutional AI papers. ✓ LinkedIn Signal-matching against career trajectory model. ✓ Conferences NeurIPS, ICLR, ACL speakers and workshop contributors. Matches found: 47 candidates (tier A–C) Shortlist: 12 (acceptance probability > 0.72) Est. time-to-offer: 18 days λ
Every engagement follows the same four-stage framework — because rigour isn't optional when outcomes are guaranteed.
30–45 minute call. No pitch, no deck. We map your systems, data landscape, team capabilities, and the specific metric you want to move. We tell you honestly whether we can help.
We design the solution to your constraints — budget, timeline, existing stack, compliance requirements. Nothing generic. Everything documented and reviewed before build begins.
Production-grade from day one. Not a proof of concept. We build with observability, monitoring, and rollback capabilities baked in — because AI systems in production need to be owned, not watched.
Measure → improve → measure again. We don't close the engagement until the target metric moves. Then we document what drove the outcome and train your team to own it.
Every day without a coherent AI strategy is a day your competitors compound their advantage. 30 minutes. No commitment. Let's map what's possible.