See how Splashgain combines vision, document processing and AI agents to power Eklavvya, DocuExprt and ePravesh, with integrations into your existing systems.
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Explore AI agent development servicesWhat agentic AI is
Each layer contains the one inside it. The four words get swapped around in vendor decks — they are not the same thing, and the gap between the second and the fourth is what you are actually buying.
Runs the whole job. Several agents, one goal, guardrails, and a way back.
Acts on the answer. It has tools, memory, and permission to use them.
Produces an answer. Ask it, and it writes, scores or summarises.
Learns patterns from data. It classifies, predicts and reads.
Gen AI
What you get
Text you then act on
What it remembers
The conversation
When it is wrong
You catch it
When it runs
When someone asks
Agentic AI
What you get
A task already finished
What it remembers
Your system of record
When it is wrong
It rolls back and pages a human
When it runs
On a schedule, unattended
How it works
Every product draws on the same models. What we learn proctoring an exam improves how we read a marksheet.
Computer vision that verifies candidate identity, watches for exam-hall malpractice and reads handwritten answer sheets.
Trained on
Live proctoring sessions and scanned answer scripts from Eklavvya exam deployments — not stock footage.
Outputs
Extraction and verification models that read and validate government IDs and academic documents.
Trained on
Government-issued IDs, marksheets, certificates and admission documents processed through DocuExprt.
Outputs
Models that assess free-form work and generate the instruments used to test it.
Trained on
Manually evaluated answer sheets — evaluators grade the first 20–25% of a batch, and the model learns the marking pattern before grading the rest.
Outputs
The layer that connects the platform to the systems an institution already runs — as an API, a no-code automation, or an AI agent.
Trained on
Built on documented REST endpoints, webhooks and Model Context Protocol servers shared across all three products.
Outputs
The agentic stack
Frontier LLMs — served through Vertex AI and Azure AI services — power descriptive answer evaluation, question paper generation, AI interviews and counseling agents. The platform is model-agnostic by design: the right model per task, swapped without rewiring the product.
Agents do not run alone. Self-hosted n8n engines, MCP servers and documented REST APIs orchestrate multi-step processes — retrieval, evaluation, verification, notification — across all three products and the systems an institution already operates.
The workflow around the AI is automated end to end: answer-sheet ingestion, document pipelines, admission rounds, result publication and follow-ups run as scheduled, monitored automations, with WhatsApp and email dispatched by the same agents — so a candidate hears about a result without anyone sending the message. Humans review the exceptions instead of executing every step.
Computer vision, OCR and generative evaluation models trained on the platform’s own production data — proctoring sessions, answer scripts and verified documents — not stock datasets, so accuracy is measured against real institutional workloads.
How we build it
Agentic software does not behave like the software before it — ask the same question twice and you can get two answers. Putting that into an exam hall or an admissions office takes more than a model. These four disciplines are what our engineering team is held to.
An LLM’s output is a probability, not a guarantee, so evaluation is engineering here rather than QA. Every agent ships against a scored eval set drawn from real institutional data, and error analysis runs on the cases it got wrong, not the ones it got right. On descriptive evaluation that is literal: evaluators grade the first 20–25% of a batch, and the model is measured against their marking pattern before it touches the rest.
Most of what makes an AI system trustworthy is ordinary engineering — cost, latency, failure modes, data residency, who is allowed to see which record. Knowing those tradeoffs is what lets us stay model-agnostic: the right model per task, swapped without rewiring the product, because the architecture around it was built to allow the swap.
Our engineers build with coding agents daily, under the same discipline we sell: tight context, a verifier so the agent can close its own loop, and a named human accountable for what merges. That is why capability lands in weeks instead of quarters — and why no agent is ever pointed at production data unsupervised.
Given a clear spec, agents deliver. Deciding what belongs in the spec is the harder half, and it runs on your context, not ours — how an exam board actually clears a result, what an admission round has to finish by. We shape the problem with your team, put a narrow version in front of real users early, and slow down deliberately where being wrong is expensive.
Underneath all four is an assumption that this field keeps moving. What we ship next year will not be built the way this was.
Reporting
Scores, competencies and throughput come out of one analytics engine, so an exam result, a verified document and an admission round are all measured the same way and can be read side by side.
Ready to see it in action?
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Infrastructure and integrations
Splashgain is a partner of both Google Cloud and Microsoft Azure. Google Kubernetes Engine already carries exams at national scale, agent and automation workloads run on DigitalOcean and n8n, and the platform meets your stack through documented APIs rather than a migration.
Google Cloud — partner
GKE runs up to 50,000 concurrent exams at 100% uptime; Vertex AI and Vision AI extend evaluation and proctoring
Microsoft Azure — partner
Azure compute and AI services running alongside Google Cloud
DigitalOcean
Containerized AI agents and automation services
n8n workflow engines
Self-hosted automation orchestrating agent workloads behind every product
Multi-provider S3 storage
Documents and recordings distributed across multiple S3-compatible providers to scale
MongoDB
Document store behind agent state, workflow runs and unstructured assessment data
BigQuery
Rolling out as the assessment analytics warehouse
REST API
Documented endpoints with webhooks
n8n
Self-hosted workflow automation
Make
No-code system-to-system automation
MCP
Model Context Protocol servers for AI assistants
WhatsApp Business API
Candidate and applicant messaging — reminders, status and results
Email automation
Transactional and bulk mail on Amazon SES — verification, reminders, results and follow-ups, sent and tracked by agents
Security
The platform runs multi-cloud on Google Cloud and Microsoft Azure with a MeitY-empanelled security audit behind it. Data residency, encryption and retention are configured per customer rather than assumed.
Security
CERT-IN Certified
Which product do I need?
Assessment, proctoring and evaluation
40M+Assessments delivered
Explore Eklavvya
Document verification and automation
99.5%Extraction accuracy
Explore DocuExprt
Admissions and enrollment
2M+Applications processed
Explore ePraveshPrefer to see the agents on their own? Browse the AI employees and agent packs.
Questions
An AI agent is one worker: it takes a goal, picks a tool, does a step and reports back. Agentic AI is the system around several of them — decomposing a goal into steps, handing work between agents, holding state across a process that runs for days, and rolling back cleanly when a step fails. One agent answers an admission query. An agentic system runs the admission round.
No. A chatbot returns text and leaves the work to you. These agents write to the systems you already run — verifying a document, releasing a result, escalating a ticket — inside permissions you set, with every action logged and reversible. If it can only talk, it is not an agent.
Free presentation · 13 slides · PDF
The presentation we walk boards and CXOs through — thirteen slides on what deploying AI agents actually involves, from the roles to the guardrails to the commercials.
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