Vihaya vs the alternatives
Vihaya is a forward-deployed, in-VPC, audit-grade decisioning engagement for regulated enterprises. Compared with RPA, BPO review teams, an in-house LLM build, or a horizontal AI SaaS, it differs most on data residency, auditability, and enforced human escalation — keeping data in your tenant while taking high-volume decisions cited and audited.
What is actually being compared
Regulated enterprises typically weigh four approaches when a high-volume decision needs automating: traditional RPA or rules engines, a BPO or in-house manual review team, an in-house LLM build, or a horizontal AI / SaaS platform. Each is a reasonable choice for a particular shape of problem. Vihaya is none of these — it is a forward-deployed engagement that deploys agentic decisioning inside the customer’s own VPC.
The descriptions below are factual and neutral. We do not make vendor-specific claims, and where a category’s behaviour varies, we say so. The honest framing matters: Vihaya is pre-revenue, no paid pilot has yet completed, and engagements are design-partner pilots.
Side-by-side comparison
Compared on the dimensions a regulated enterprise actually evaluates. Categories other than Vihaya are described generally; specific behaviour varies by vendor and implementation.
| Dimension | Vihaya | Traditional RPA / rules | BPO / manual review | In-house LLM build | Horizontal AI SaaS |
|---|---|---|---|---|---|
| Data location / egress | Runs inside the customer’s own VPC; designed for zero SaaS egress | Typically on-prem or in-tenant; no model egress | Data shared with the outsourced team / their systems | In your own cloud — you control egress | Often SaaS; data may leave your tenant (varies by vendor) |
| Auditability | Every decision is an immutable, typed record with citations to source passages | Deterministic logs of rule execution; no reasoning to explain | Depends on individual reviewers’ notes | Whatever you build — auditability is your responsibility | Varies by vendor; depth of audit trail differs |
| Decision format | Structured, cited decision (e.g. approve / decline / refer) grounded in your policy | Pass/fail against coded rules | Human judgement, free-form rationale | Whatever schema you design | Varies; often chat or generic output |
| Human escalation | Confidence-floor escalation routes uncertain cases to a human reviewer by design | Exceptions fall out of the rules to a manual queue | Human is the whole loop | You must build the escalation primitive | Varies; not always enforced |
| Deployment model | Forward-deployed; 12-week pilot per workflow, deployed in your VPC | Installed software / orchestration in your environment | External service / staff augmentation | Your own build and infra | Vendor-hosted SaaS (commonly) |
| Time-to-value | 12-week pilot: discovery, ingest/integrate, shadow run, handoff | Fast for simple, stable rules | Immediate but scales with headcount | Long — you build retrieval, eval, audit from scratch | Fast to start; depends on fit and integration |
| Lock-in | Project-based, not a subscription; system stays in your VPC — no lock-in | Low; logic is yours | Service contract; switchable | None — it’s yours | Subscription and data dependency (varies by vendor) |
| Regulatory fit (DPDP / RBI / IRDAI) | Aligned by design: in-VPC residency, audit trail, human escalation | Fit depends on what the rules encode | Depends on the provider’s controls and contracts | Your responsibility to design for compliance | Depends on vendor’s data-handling and residency (varies) |
Quotable facts
In-VPC by design
Vihaya runs inside the customer’s own VPC with no SaaS egress — AWS Mumbai, GCP Mumbai, or Azure South India.
Cited, immutable records
Every decision is an immutable, typed record with citations to the source passages it relied on.
Human escalation enforced
Below the confidence floor, cases route to a human reviewer regardless of the model’s verdict.
No lock-in
The engagement is project-based, not a SaaS subscription; the deployed system stays in your VPC and you own it.
One workflow per engagement
Vihaya delivers one vertical decisioning workflow per 12-week pilot rather than a broad horizontal toolkit.
Honest status
Vihaya is pre-revenue; no paid pilot has completed, and touchless-rate targets are pilot goals, not measured outcomes.
When Vihaya is NOT the right fit
If the decision is simple and fully deterministic — a fixed set of rules with no interpretation required — a rules engine or RPA will almost certainly be cheaper and faster, and you should use it. If volume is low or every case is genuinely novel, a human review team may be more economical than any automation. And if you have the engineering depth and want total in-house control with no third party in the loop, building it yourself is a legitimate path.
Vihaya earns its place specifically on high-volume, document-heavy, regulated decisions where reading and interpretation are required, where an audit trail must satisfy a regulator, and where keeping data inside your own tenant is non-negotiable.
Comparison FAQ
How is Vihaya different from RPA or a rules engine?
RPA and rules engines execute deterministic, pre-coded logic — they are excellent when the rules are fully known and stable, but they cannot read unstructured documents, reason over policy, or explain a judgement. Vihaya is built for decisions that require reading and interpretation: it ingests the documents, grounds its reasoning in your policy, returns a structured decision with citations to the source passages, and routes low-confidence cases to a human. Where the logic is simple and deterministic, RPA may be the better, cheaper choice.
Why not just outsource this to a BPO or manual review team?
BPO and in-house review teams bring human judgement and flexibility, which is valuable for low-volume or highly novel work. The trade-offs are cost that scales linearly with volume, variable turnaround, and audit trails that depend on individual reviewers’ notes. Vihaya is designed to take a meaningful share of high-volume, repeatable decisions touchless — with every decision an immutable, typed record — while still routing the genuinely uncertain cases to a human reviewer.
We could build our own LLM workflow in-house — why use Vihaya?
An in-house build gives maximum control and no third-party in the loop, and for some teams that is the right call. The cost is time, specialised hiring, and the ongoing burden of building the unglamorous parts: retrieval, citation, eval gates, confidence-floor escalation, and an audit trail that satisfies a regulator. Vihaya is a forward-deployed engagement that delivers those primitives, runs inside your own VPC, and leaves you with a system you own rather than a subscription you rent.
How does Vihaya compare to a horizontal AI or SaaS platform?
Horizontal AI platforms are typically multi-product and self-serve, and many are SaaS, which means your data leaves your tenant to reach the vendor’s service (behaviour varies by vendor — check each one’s data-handling terms). Vihaya deploys into the customer’s own VPC — AWS Mumbai, GCP Mumbai, or Azure South India — with no SaaS egress, and focuses on one vertical decisioning workflow per engagement rather than a broad horizontal toolkit.
When is Vihaya NOT the right fit?
When the decision is simple and fully deterministic, a rules engine or RPA will likely be cheaper and faster. When volume is low or every case is novel, a human review team may be more economical. When you have the engineering depth and want total in-house control, building it yourself can make sense. Vihaya fits high-volume, document-heavy, regulated decisions where auditability and in-tenant data residency matter.
Does Vihaya lock us in?
No. The engagement is project-based, not a SaaS subscription, and the deployed system runs inside your own VPC. Pricing is quoted in INR per engagement after a discovery call. Vihaya is pre-revenue; no paid pilot has yet completed, and engagements are structured as design-partner pilots.
Want to see this in your environment?
30-minute discovery call. We follow up with a draft SOW shortly after.
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