Four production-grade AI products — each solving a distinct operational problem. No dashboards for dashboards' sake. Everything ships with measurable outcomes and 90-day ROI targets.
Most organisations discover process failures through consequences — a missed deadline, a support ticket spike, a revenue shortfall. Kairos Meridian inverts that. It maps your operational workflows in real time, identifies emerging bottlenecks before they cascade, and surfaces the precise intervention — with predicted impact — so your team can act rather than react.
Event-stream ingestion from your existing systems — ERP, CRM, ITSM, custom APIs — assembled into a live process graph. Every step, every handoff, every wait state tracked without modifying your source systems.
Transformer-based sequence models trained on your process history learn what "normal" looks like — then flag deviations as they emerge, not after they've compounded. Sub-process granularity. Zero tuning required after initial calibration.
Not just "there's a problem here." For every detected anomaly, PIH generates a ranked list of interventions with predicted outcome distributions — drawn from 10,000+ simulated process variants. Your team decides; PIH informs the decision.
A single-pane operational view built for the people running the business, not the engineers running the models. Configurable by role — executives see throughput and cost; operations managers see step-level diagnostics; IC teams see task queues.
Pre-built connectors for SAP, Salesforce, ServiceNow, Jira, Zoho, and 40+ SaaS platforms. REST and webhook APIs for custom systems. Bi-directional write-back for triggering remediation actions directly from the PIH interface.
Automated weekly and monthly process health reports with variance analysis, root-cause attribution, and trend forecasting. Board-ready export formats. Audit trails for every model decision, stored immutably for compliance review.
λ pih scan --workflow=order-to-cash --depth=full Scanning process graph... 847 nodes, 1,204 edges Event window: last 30 days (2.4M events) Baseline model: v4.2 (calibrated 2026-03-14) ───────────────────────────────────────────── ANOMALY REPORT ───────────────────────────────────────────── ⚠ HIGH Invoice approval loop Step 7 → 9 → 7 cycle rate: +340% vs baseline Projected cost impact: ₹4.2L/month Recommended intervention (confidence 0.89): → Auto-escalate invoices > ₹50k after 48h wait → Predicted cycle reduction: 73% ✓ NORM Procurement initiation p95: 2.1h ✓ NORM GRN matching accuracy: 99.1% ✓ NORM Vendor payment batch on-time: 97.4% ───────────────────────────────────────────── Overall process health score: 82/100 (+6 MoM) λ
Hiring decisions are among the highest-leverage choices an organisation makes — and among the most error-prone. Kairos Vantage applies predictive candidate modelling, multi-source signal aggregation, and retention risk analytics to turn talent acquisition from a gut-driven process into a repeatable, data-grounded system. Hire faster, retain longer, spend less.
Multi-source candidate profiling across LinkedIn, GitHub, portfolio sites, academic databases, and referral networks. NLP-powered competency extraction — beyond keyword matching — with team-culture-fit scoring against your organisational embeddings.
Role-specific question sets generated from your competency framework and the candidate's signal profile. Real-time prompts for interviewers. Post-interview transcript analysis with scoring rubrics — reducing inter-rater variability by up to 60%.
Continuous post-hire tracking: performance trajectory, engagement signals, compensation benchmarking, and peer-network analysis. 90-day churn risk scores updated weekly. Early-warning alerts to managers before retention problems become departures.
A living, scored talent pool — not a static ATS. Every candidate interaction, every signal update, every rehire potential flag tracked automatically. Warm-pipeline nurture sequences triggered by role openings, reducing time-to-first-outreach from weeks to minutes.
Real-time screening parity monitoring across demographic cohorts. Disparate impact analysis at every funnel stage. Explainability logs for every rejection — giving HR teams the audit trail that increasingly strict hiring regulations require.
12-month headcount demand models built from your growth trajectory, attrition history, and market hiring velocity. Scenario modelling for M&A, restructuring, or rapid scale. Builds the business case for proactive hiring rather than reactive backfilling.
λ hims search --role="Senior ML Engineer" --level=IC4 Loading competency profile... role_ml_ic4_v3 Culture embedding: org_kairos_2026q1 Scanning sources: ✓ GitHub Repo quality, contribution graph, OSS impact ✓ LinkedIn Trajectory model + team-fit signal ✓ arXiv Published authors matching domain ✓ Warm pool Previous applicants, score ≥ 0.68 ───────────────────────────────────────── TOP MATCHES ───────────────────────────────────────── #1 Priya S. fit: 0.91 churn-risk: LOW #2 Arjun M. fit: 0.88 churn-risk: LOW #3 Leila F. fit: 0.85 churn-risk: MED Est. time-to-offer: 18 days Avg market time-to-offer: 54 days λ
Business intelligence tools show you what happened. Kairos Oracle tells you what's about to happen and what to do about it. Predictive models, real-time anomaly detection, and natural language querying combine into a decision-support layer that makes every person in your organisation analytically capable — regardless of their technical background.
Purpose-built forecasting models for revenue, demand, churn, and operational metrics. Ensemble methods (XGBoost, LightGBM, Prophet) with automatic feature engineering. Uncertainty quantification baked in — you see prediction intervals, not just point estimates.
Streaming anomaly detection across your KPIs with sub-minute latency. Isolation Forest and LSTM autoencoders detect both point and contextual anomalies. Configurable sensitivity by metric criticality — no alert fatigue, no missed signals.
A conversational layer over your data warehouse. Ask "What drove the revenue drop in Karnataka last quarter?" and receive a structured breakdown with contributing factors ranked by impact. No SQL required. Answers grounded in your actual data, not AI confabulation.
Role-aware dashboards that surface the most decision-relevant metrics by user context. Auto-layout adjusts as data volumes and business questions evolve. Drag-and-drop composition for custom views — no analyst required for dashboard changes.
A single consistent definition of every business metric — revenue, margin, churn, CAC — applied uniformly across all sources. Eliminates the "which number is right?" problem. Built on dbt-compatible transformation logic with full data lineage tracking.
Go beyond correlation. Causal graph analysis attributes metric movements to upstream drivers — separating signal from noise in complex multi-variate environments. Tells your team not just that revenue dropped, but precisely which factors drove it and by how much.
λ ask "Why did South revenue drop 18% in April?" Parsing intent... causal analysis query Sources: sales_db, crm_events, ops_logs Time range: 2026-04 vs 2026-03 baseline Running causal attribution model... ───────────────────────────────────────── CONTRIBUTING FACTORS (ranked) ───────────────────────────────────────── #1 New customer acquisition -₹12.4L Lead volume: -34% (SDR headcount -2) #2 Avg deal size -₹3.8L Discount rate: +8pp (Q-end pressure) #3 Churn / expansion +₹2.1L Net retention held steady: 107% ───────────────────────────────────────── Recommended action: Restore SDR headcount Predicted recovery: +14% in 60 days λ
Regulatory environments are no longer static. New guidelines from RBI, SEBI, MCA, and DPDPA obligations continue to evolve — while audit cycles compress and penalty regimes harden. Kairos Vigil watches every regulation relevant to your business in real time, maps your operational data against current requirements, and alerts you to gaps before they become findings.
Automated scraping and NLP parsing of regulatory publications from RBI, SEBI, MCA, IRDAI, DPDPA authority, and international frameworks (GDPR, SOX, ISO 27001). Change taxonomy applied within hours of publication — mapped to your affected business units.
Continuous testing of your operational data against your compliance control library. Every control tested programmatically — not manually — with evidence captured automatically. Gap reports generated in standardised formats aligned to audit committee expectations.
Every system action, every data access, every control test stored in an immutable, tamper-evident log. Audit package generation in one click — formatted for your specific regulator. Reduces audit preparation time from weeks to hours.
Risk scores for every process, entity, and transaction updated in real time. Concentration risk, counterparty exposure, data residency violations, and unusual pattern flags — each scored with predicted regulatory impact if left unaddressed.
Your internal policies and external regulatory requirements maintained in a single versioned library. Automatic cross-referencing identifies policy gaps when regulations change. Policy update workflows with stakeholder approval trails and rollout tracking.
Pre-built report templates for common Indian and international regulatory submissions. NLP generation of narrative sections from underlying data — your compliance team reviews and approves rather than writes from scratch. Submission scheduling and confirmation tracking.
λ compliance scan --framework=DPDPA --scope=full Framework: Digital Personal Data Protection Act 2023 Control library: 142 controls mapped Data sources: 18 connected systems Last full scan: 4 minutes ago ───────────────────────────────────────── CONTROL STATUS ───────────────────────────────────────── ✓ 134 controls PASS ⚠ 6 controls REVIEW ✗ 2 controls FAIL ───────────────────────────────────────── FAIL S7.3 Consent withdrawal mechanism Marketing DB missing unsubscribe API endpoint Remediation: Add /consent/revoke endpoint (est. 2h) NOTE New DPDPA circular published 2026-05-01 3 controls may require re-assessment Auto-review scheduled: today 18:00 λ
Every product deployment follows the same rigorous four-stage process — and includes knowledge transfer so your team owns the outcome, not just the interface.
We map your existing systems, data landscape, and the specific metric you want to move. Define integration requirements, compliance constraints, and success criteria before a single line of code is written.
Product configured and connected to your stack. Data pipelines validated, models calibrated on your historical data, and access controls mapped to your org structure. Typically 3–6 weeks for full integration.
Soft launch with your operations team. Shadow mode running before full activation — your team sees model outputs alongside existing processes, builds confidence, identifies edge cases. Hard cutover only when you're ready.
Full documentation, admin training, and 90 days of priority support included. Monthly model drift reports. Kairos engineers on retainer for tuning. You own the system — we make sure you can run it.
Every product ships with a 90-day ROI target. If we can't show meaningful movement by day 90, we extend the engagement at no cost. Book a 30-minute technical walkthrough.