We've watched too many AI engagements stall in the POC graveyard — built before the problem was understood in the context of your enterprise. 80% of enterprises now use generative AI; only one in twenty report material EBIT impact.1 We believe AI should make work easier — not harder.
It means understanding your enterprise first — before naming a solution.
It means bringing accelerators — not standard wrappers — across discovery and delivery.
It means taking a consultative approach to separate the workflows that need AI from those that don't.
It means blending AI with traditional engineering — using each where it fits.
It means optimizing effort, investment, and people across the solution stack — not just the AI on top.
Enterprise buyers in 2026 choose from five kinds of partner. Each is excellent at the slice they own. None is structurally able to deliver the outcome end-to-end. Wekalp is the bundle — one platform, plus the AI services that go with it.
| Partner | What they offer | Wekalp — Platform + AI Services, in one box |
|---|---|---|
| Cloud-scale lakehouse incumbents | Storage + compute. You assemble the catalog, lineage, quality, governance, context, agent runtime — and find the pod yourselves. | Lakehouse + every layer above it, native in one box. Plus the pod that builds on it, end to end. |
| Best-of-breed catalog, quality, and transformation specialists | One slice each. You stitch together five other tools, the integration tax, and the agent layer entirely. | Every slice native, in the same box. Plus the pod that owns the substrate. |
| Pure-play AI delivery firms · new-wave agent specialists | A pod that ships agents on a data substrate they assume already exists. Most pilots quietly stall there. | The substrate is the box — purpose-built for agents. The pod ships them in < 2 weeks. |
| The global systems integrator majors | Implementation hours and a managed services tower — on top of the same six-vendor stack you'd be licensing anyway. | One platform replaces the stack. The pod is priced to the outcome, not to the hours. |
| Hyperscaler-native AI platforms | Credits, models, tooling — and no opinion on your workflow. You bring the pod. | Models routed through governed enterprise context. The pod brings the workflow. |
Against a typical six-vendor stack assembled for equivalent scope: Wekalp delivers a 30–40% reduction in three-year TCO — before counting the SI hours not needed for the assembly.
Wekalp is a Data Platform + AI Services company. Sold as one bundle — not two line items. Both halves are designed for each other.
Single-box. AI-native. Storage to agent runtime — natively integrated.
A small, senior pod — forward-deployed, end-to-end accountable, with your SME embedded inside it.
The only credible answer to "who owns the outcome end-to-end" is a partner who owns both the platform and the pod.
The traditional model splits delivery into discovery, implementation, and managed services — with separate teams, separate contracts, and a fifteen-person factory between you and the outcome. We collapse that into four stages, with the same small team running through all of them. The person who scopes the work is the person who builds it and runs it. More on this in why the forward-deployed engineer has been misdefined.
A 45-minute working session on a workflow you have been trying to fix. We diagnose what will and won't work as AI, where the data underneath is the real constraint, and what the realistic outcome is worth. You leave with a one-page map and a number.
A 2–3 week sprint. We start with the spreadsheets, reports, SOPs and mail trails the business already runs on, and reverse-engineer the workflow that lives inside them — the one nobody documented. You get a phased plan: what we build first, what it costs, what it returns, what depends on what.
Diagnostic separates every workflow into two parts. The deterministic part — validations, calculations, lineage, reporting — is built with proven engineering, the cost-efficient way to do work that does not need AI. The interpretive part — synthesis, exception handling, narrative — is matched to patterns in our agent library and adapted to your context.
The workflow goes live in weeks. The agents follow one to two weeks after. Any code we write stays small, focused, and ours to maintain — never a codebase you inherit.
Same team, sized down. The platform handles the technical operations. New requirements arrive as new agents and rule changes, not as change requests. Tomorrow's question gets answered tomorrow — not next quarter.
One team, one contract, one accountability — from triage through steady state. The fair question this raises is how a team this small can deliver what the old model staffed at fifteen. The next section is the answer.
The compressed timelines aren't heroic effort. They are the result of six pieces of internal tooling that absorb the work the old model spread across a discovery phase, an implementation factory, and a managed services tower. Each one came out of patterns we kept seeing repeat across engagements. Each one is why the pod stays small as the work scales.
What the SOPs say and what people actually do are rarely the same. Our tooling reads the documents the business already runs on — spreadsheets, reports, policy documents — and reconstructs the workflow that actually exists.
AI cannot be trusted before the data underneath is in usable form. The Workbench maps the business structures identified in discovery to their source definitions, and builds the transformation logic that makes them production-ready.
An AI is only as good as its understanding of what your business means by its own terms. The Fabric holds those definitions in one place — versioned, kept current, owned by the business.
A business workflow has to convert into a technical sequence before it can run — which artifact gets ingested first, which report runs when, which dependencies must clear before the next step. Process Sequence is the translation between the two.
Data engineering that used to take fifteen people three quarters — ETLs, logic, aggregation, data modelling — is generated by Auto Coder in minutes. Engineer-reviewed, deployed in days. The single largest reason the pod stays small.
Workflows that look bespoke to a buyer repeat with surprising consistency across industries. We have built each one once, generalised it, and now reuse it. Your engagement starts from a 70% template, not a blank page.
The agent is only as useful as the interface your business actually uses every day. We give you two options. Both run against the same substrate, the same context, the same lineage.
Your team is already in Claude or ChatGPT. We expose Wekalp's enterprise context, governed data, and agent capabilities as MCP servers. Users get authorized, lineage-backed answers — without leaving the assistant they already use. Identity flows through. Permissions are honored. Every answer is traceable to the row of data it came from.
For teams that need a controlled enterprise interface — audit trail, role-based access, data residency inside your own VPC — Wekalp provides a turnkey conversational front end on the hyperscaler your enterprise has standardized on: Amazon Bedrock, Google Vertex AI, or Azure AI Foundry. Model-agnostic. Governance in the loop. Deployed inside your perimeter.
Forty-five minutes against one of your in-flight workflows. We tell you which parts should and should not be agents. You walk away with a one-page triage map, an outcome estimate, and zero obligation either way.