We build AI into the operations an enterprise already runs.
Most engagements start with a queue, a case file, or an approval chain that people work through by hand. We map it, automate the part that repeats, and keep a reviewer on the decisions that need one. Three of the products we sell are our own and we run them ourselves, so we do not propose an architecture we have not operated.
What we do, and where the first engagement usually starts.
Most engagements start with a queue, a case file, or an approval chain that people work through by hand.
Document Intelligence & Decision Automation
Claims files, loan applications, tax workpapers and contracts come in as documents and leave as decisions, with the reasoning and the source page recorded against each one. We build on Viveka, our own document-intelligence platform, which we operate in production.
AI Enablement of Existing Systems
You do not have to replace a core banking, HR or claims platform to apply AI to it. We add a layer on top of the system you run today, working inside its existing access controls and data-residency terms. Nothing is migrated and nothing is retired.
Content & Brand Production
We write copy and documentation, produce explainer and training film, and build the brand and identity systems that hold them together. Some of this work is commissioned on its own. The rest is written to the same brief as the build it accompanies, so the launch material is ready when the release is.
Forward-Deployed Engineering
Our engineers work from within your operations, underwriting or finance team for the duration of the engagement. They see the exceptions, the workarounds and the spreadsheet holding the process together, and they build against those. A change raised at one sprint review is generally in use by the end of the next.
Regulated & Embedded Systems Engineering
Some automation cannot run in a public cloud. For those clients we engineer device fleets, on-premise deployments and AOSP-based hardware, and we work under confidentiality terms that often cover the client, the data and the deployment itself.
Platform & Product Engineering
Automation usually needs a system underneath it. We build that system and then run it: multi-tenant platforms, mobile applications, and IoT telemetry and control planes. Three of them are our own products, carrying our own uptime obligations, and we have been building this way since 2018.
Eight practices. One standard.
Platform & mobile engineering
Multi-tenant platforms built to survive production — secure by default, observable, and honest about cost — together with the native and cross-platform applications released alongside them, under disciplined release management and crash-free targets.
Evidence: uHRMS in production; shipped Play Store listingsIoT, embedded & AOSP
Device fleets, telemetry pipelines and control planes for solar, energy and industrial deployments, and the Android platform engineering beneath them: custom AOSP builds, OTA fleets, kiosk and signage hardware.
Evidence: SolarNets; OtaWall fleetAI, LLM & intelligent automation
Enterprise AI and intelligent automation: document intelligence, LLM evaluation engines and agentic workflows, integrated with the ERP, CRM and case systems already in place.
Evidence: VivekaProcess automation
Operational processes automated end to end: intake, verification, exception queues and quality assurance — measured on cost per transaction and cycle time.
Evidence: available on requestProduct design
Interface and systems design that survives engineering: design systems, user flows and edge-case states.
Evidence: this systemBrand & design
Positioning, identity and design systems, delivered with the website and application they have to live on.
Evidence: the Viveka brand launchCloud & reliability engineering
Cost-aware cloud design, migration and multi-region topology for regulated workloads, with the CI/CD, observability and incident management that operate beneath everything we deliver.
Evidence: our own uptime obligations across three live productsContent & video
Website copy, product messaging, documentation, product demos and explainer film — produced to the same brief as the build.
Evidence: available on requestAI systems that make decisions, not demos.
Most AI consultancies demo well and deploy poorly. Our AI practice is anchored by Viveka — our own document-intelligence platform in production — and by an internal multi-agent pipeline we run daily. We sell what we use.
Document intelligence
Pipelines that extract, classify and structure unstructured documents at production volume.
LLM evaluation engines
Model output paired with explicit gate criteria, so decisions are testable rather than plausible.
Agentic engineering
Multi-agent workflows that carry work between steps with human review where it matters.
AI-assisted design
Design and specification work accelerated by the same pipeline we run internally.
Integration
Automation that plugs into the systems you already run, not a parallel stack.
Pricing and practice, answered on the page.
Scoped AI engagements are priced against the specific evaluation-engine or document-pipeline you need, with fixed-milestone pricing and full token-cost transparency. Tell us the outcome you need and we’ll size it against your requirements.
We’re model-agnostic. Production systems are built on orchestration that lets us route between Claude, GPT-class, and open-weight models per task — chosen for accuracy, cost, and data-residency requirements, not vendor loyalty.
Yes — it’s a design requirement, not a feature. Every Viveka-pattern system pairs LLM evaluation with explicit business rules and gate criteria, producing decision trails an auditor can follow.
Unsure which practice applies? Most enterprise engagements draw on three or four. Describe the outcome you need and we will map the practices to it.
Begin with the process you would automate first.
A short description is sufficient to begin a conversation. We respond within one business day, and where an evaluation calls for an architecture walkthrough rather than a portfolio review, we will arrange it.