We don't sell AI. We build the specific, measurable capability your organisation needs — then we stay until it performs. Six practices, from productivity and analytics through to labour code audits and programme delivery. 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.
>> 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 >>
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.
>> 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 >>
India has consolidated twenty-nine central labour laws into four Codes — the Code on Wages 2019, the Industrial Relations Code 2020, the Code on Social Security 2020, and the Occupational Safety, Health & Working Conditions Code 2020. They redefine what counts as "wages", rewrite contract-labour and principal-employer obligations, re-scope who is an employee, and move gratuity, PF and bonus exposure in ways most finance teams have not yet modelled.
Most organisations discover the gap during an inspection. We audit your establishments against all four Codes and the applicable state rules, quantify the financial exposure, and hand back a prioritised remediation plan with owners, effort and cost attached — not a list of observations.
Book a labour code auditEvery location, every entity, every worker category tested against the four Codes and the state rules that apply to it — wage structure, working hours, overtime, leave, registers and returns, licensing, safety obligations, and the standing orders threshold. Findings graded by severity and statutory exposure.
The new definition of wages changes your PF, gratuity and bonus liability before it changes anything else. We model the impact across your entire compensation structure — employee by employee, band by band — so you see the P&L number before you commit to a restructure, not after.
Contract labour is where the liability hides. We audit vendor licensing, wage compliance down the chain, ESIC and EPFO remittances, PoSH and safety obligations, and the principal-employer exposure you inherit when a contractor fails — with contract redlines to close it.
>> kairos audit --labour-codes --entity "all" --state "KA,MH,TN" Testing establishments against four Codes... ✓ Wages 2019 Wage definition · 50% rule · payment timelines · deductions. ✓ Industrial Rel. 2020 Standing orders · notice periods · works committee. ✓ Social Security 2020 PF · ESIC · gratuity · gig & platform worker scope. ✓ OSH Code 2020 Licensing · contract labour · hours · welfare facilities. Gaps identified: 23 (4 critical · 9 major · 10 minor) Exposure modelled: Wage restructure impact quantified across 3 states Remediation plan: owners & dates assigned · 90-day close-out >>
Programmes rarely fail on technology. They fail on ownership — decisions that never get made, scope that drifts quietly, dependencies nobody is tracking, and a steering committee that learns the truth a month too late. Kairos Orbit puts an experienced delivery lead inside your programme and holds the line: plan, governance, risk, vendors, and one honest status that everybody trusts.
An AI rollout, an ERP or HRMS implementation, a compliance remediation programme, a post-merger integration, or a PMO stood up from nothing — we run it end to end, or we run alongside your team until they can run it themselves. The engagement ends when the capability transfers, not when the invoice clears.
Talk about a programmeCharter, scope boundaries, a plan that survives contact with reality, a resourced delivery schedule, a RAID log that is actually maintained, and a benefits map that ties every workstream back to the number it is supposed to move. Baselined in two weeks, defended for the duration.
Steering packs your sponsors read, decision logs with named owners and dates, dependency and critical-path management across teams, change control that says no when no is the right answer, and vendor management that holds third parties to the contract you signed.
A PMO you rent rather than build — templates, reporting rhythm, portfolio view, stage gates and estimation discipline. We install the machinery, run it while your team learns it, then hand over the keys and the documentation and get out of the way.
>> kairos status --programme "HRMS Rollout" --week 9 Baseline vs. actual... ✓ Schedule 14 of 18 milestones on or ahead of baseline. ✓ Budget Burn at 47% against 51% elapsed. No re-forecast required. ! Dependency Payroll data migration blocked on vendor sign-off (D-12). ✓ Decisions 6 open · 4 due this week · owners assigned, no drift. Critical path: protected · 5 days float remaining Escalations: 1 to steering — vendor SLA breach, remedy proposed Status: GREEN — honestly, and with the evidence attached. >>
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.