Discovery sprint
2–3 weeks
Validate the AI bet before committing the budget
- Workflow mapping with your domain leads
- Data + integration audit
- Working prototype on a single use case
- Risk register + ROI model
- Go/no-go memo for the build phase
AI Transformation Services
From slide-deck to production AI in eight weeks.
Splendens runs the transformation arm so your AI bets actually ship. Discovery to operate, with weekly demos, outcome-gated milestones, and a co-build culture instead of a vendor-customer wall.
Diagnose · 2 wksArchitect · 2–3 wksBuild · 8–16 wksOperate · ongoingCo-build with your engineers
- 40+
- Programs delivered
- 12
- Industries served
- 8 wks
- Median time-to-outcome
- 99.95%
- Production uptime SLA
AI Transformation Services
From boardroom slide to production AI in eight weeks
Most AI engagements stall in the proof-of-concept loop. Splendens runs four crisp phases — diagnose, architect, build, operate — each with hard outcome gates, not slide-deck status updates. You get measurable production AI, not a research report.
- Phase 01
Diagnose
2 weeks
- Workflow shadowing with your domain leads
- Data, integration, and risk audit
- AI-readiness scorecard scoped to your industry
- ROI model — what's worth automating, what isn't
- Phase 02
Architect
2–3 weeks
- Reference architecture written to your IT standards
- Threat model + responsible-AI guardrails
- Eval harness drafted against real labelled data
- Sprint plan + outcome-gated milestones
- Phase 03
Build
8–16 weeks
- Two-week sprints, weekly demos with your team
- RAG / agent / model pipelines wired to your data
- Observability + CI/CD baked in from day one
- Internal canary, then phased production rollout
- Phase 04
Operate
Ongoing
- On-call MLOps + 24/7 model observability
- Quarterly eval re-runs against labelled examples
- Cost & latency optimisation cycles
- Roadmap reviews aligned to your OKRs
40+
Transformation programs delivered
12
Industries served end-to-end
8 wks
Median time-to-first-outcome
99.95%
Uptime SLA on production AI
How we engage
Three engagement tiers, one delivery culture
Pilot to validate, build to ship, scale to operate. Most programs start with a 2-week discovery sprint and graduate from there once the first outcome is on the board.
Build engagement
8–16 weeks
Production-grade rollout, co-built with your engineers
- Architecture + threat-model review
- Sprint-by-sprint delivery with weekly demos
- RAG / agent / model pipelines wired into your data
- Eval, observability, and CI/CD baked in
- Production hand-off with runbooks
- Outcome-tied milestones, not time-and-materials
Scale & operate
Ongoing
Run, evaluate, and continuously improve in production
- On-call MLOps + 24/7 model observability
- Shadow-deploy + canary release patterns
- Quarterly evaluation harness re-runs
- Cost & latency optimisation cycles
- Roadmap reviews with your product leads
Sample 12-week build
What if you signed on Monday?
Real Splendens engagements vary in scope, but here's the cadence most production builds run at. Discovery + architecture + four sprints + canary + scale, with hard outcome checkpoints every two weeks.
- W1 – W2step 1
Discovery sprint
- Workflow shadowing with your domain leads
- Data + integration audit
- Working prototype on the highest-leverage use case
- Risk register, ROI model, go/no-go memo
- W3 – W4step 2
Architecture + threat model
- Reference architecture written to your IT standards
- Security & compliance review with your CISO team
- Eval harness drafted against real labelled examples
- Sprint plan locked, demo cadence set
- W5 – W8step 3
Build sprints
- Two-week sprints with weekly demos
- Production-bound RAG / agent / model pipelines
- Observability + CI/CD wired in from day one
- Eval harness re-runs every release candidate
- W9 – W10step 4
Internal canary
- Shadow-deploy to internal users
- Latency, cost, and quality dashboards live
- Bug bash + UAT with your domain leads
- Runbooks, on-call rota, escalation paths defined
- W11step 5
Production launch
- Phased rollout — 10% → 50% → 100%
- Blameless retros after every increment
- Real-time SLO dashboard handed to your SRE team
- W12+step 6
Scale & operate
- Quarterly eval + cost-optimisation reviews
- Roadmap reviews aligned to your OKRs
- On-call MLOps if you want us in the rotation
Industries we transform
Vertical depth matters. We embed with your experts so models reflect real workflows — not slide decks.
Healthcare & life sciences
Clinical copilots, prior-authorization automation, and compliant LLM workflows over structured records.
Financial services
Risk scoring, document intelligence, and fraud signals with audit trails and explainability baked in.
Manufacturing & supply chain
Vision QA, predictive maintenance, and demand forecasting tied to your ERP and IoT streams.
Retail & logistics
Personalization engines, assortment optimization, and last-mile intelligence without black boxes.
Energy & utilities
Grid analytics, safety monitoring, and operations copilots grounded in domain ontologies.
Education & talent
Adaptive learning systems, skills inference, and AI-assisted content grounded in your curriculum.
In production
Three deep-dives, three live deployments
We don't write case studies for slide decks. Each story below is a Splendens-built system running today, paid for by real customers, with operating metrics we can show.
Healthcare
AI-native therapy operations across 300+ centres
Challenge
Therapy networks were stitched together with shared spreadsheets, generic CRMs, and per-clinic Word docs. Sessions ran fine — everything around them didn't.
What we built
BloomSenz Platform: a dual-sided application where therapists, audiologists, and front-desk staff coordinate through one workflow while families see a clean, app-grade window into their child's progress.
Outcome
10K+ sessions orchestrated · 4.9★ care-team feedback · same audit log every regulator asks for.
Education
Eductate — the AI mentor for India's biggest competitive exams
Challenge
Aspirants for NEET, JEE, UPSC, TNPSC, and TET drown in static PDFs and pre-recorded courses. They don't need more content — they need a system that watches their attempts and tells them what to revise tonight.
What we built
Eductate (built on BloomLearn): adaptive mock tests, AI mentor with per-topic accuracy, doubt solver, daily plan with missed-task recovery, and verified teacher chat — all under one app.
Outcome
12,400+ aspirants · +38 avg mock-score lift in 8 weeks · 4.8★ App Store rating.
Retail & marketplace
End-to-end commerce for fragmented seller ecosystems
Challenge
Multi-brand marketplaces and pet-care networks needed seller onboarding, catalogue intelligence, and last-mile fulfilment without bolting six SaaS tools together with brittle webhooks.
What we built
BloomCommerce + BloomPetos: marketplace spine with seller dashboards, AI-augmented discovery, payouts, and recurring-care prompts — operators run promos, returns, and inventory from one cockpit.
Outcome
10K+ active sellers · 12K+ monthly orders · 4.8★ buyer-app sentiment.
Trust, security & responsible AI
The same rigor we apply to models applies to how they're hosted, accessed, and governed — so legal, risk, and engineering stay aligned.
GDPR-ready workflowsSOC 2–aligned controlsAudit logs by defaultVendor DPAs available
Encryption everywhere
TLS 1.3 in transit, KMS-backed keys at rest, and tenant-isolated secrets — aligned with your InfoSec checklist.
Identity & least privilege
SSO / OIDC, RBAC, and scoped service accounts so humans and agents only touch what they must.
Private & air-gapped paths
VPC peering, dedicated tenancy, and on-prem inference where regulation demands it.
Compliance roadmap
GDPR-aware flows, HIPAA-aware deployments for healthcare, and SOC 2 Type II–aligned controls with audit trails.
Responsible AI & governance
Shipping fast doesn't mean shipping blind. We embed evaluation, oversight, and traceability into the release path — not as an afterthought.
- ✓
Evaluation & benchmarks
Task-specific scorecards, regression suites on golden sets, and domain-expert rubrics — not a single accuracy number.
- ✓
Adversarial review
Prompt injection and jailbreak testing, plus scenario libraries mapped to your abuse policies.
- ✓
Human oversight
Configurable review queues when confidence is low or stakes are high — auditable decisions.
- ✓
Lineage & reproducibility
Dataset versioning, model cards, and deployment manifests so every answer traces back to a known artifact.
Ready to shape your next AI chapter?
Tell us about your domain, constraints, and timelines. We'll respond with a concrete path — workshop, pilot scope, or referral if we're not the right fit.
Typical response within two business days · NDAs welcomed