Eight systems. One foundation.

Problem

Most AI products fall apart under real load — they're demos with no error handling, no state management, and no observability.

Approach

We built a model-agnostic microservices foundation: persistent state in PostgreSQL, Redis for sessions and queues, WebSocket/SSE streaming, and full distributed tracing — before a single LLM call is made.

Outcome

Every Keyved product runs on this layer, so each new build starts hardened, observable, and ready to scale instead of starting from scratch.

Problem

Finance teams lose days to manual reconciliation, miss fraud patterns hidden in transaction noise, and run slow, inconsistent due-diligence cycles.

Approach

We built explainable AI agents on top of a time-series data layer — trading signals with backtesting, chargeback anomaly detection, cross-ledger reconciliation, and a document-analysis agent — each decision logged and auditable.

Outcome

Reconciliation that took a team days now runs in a fraction of the time with a cleaner audit trail, and fraud patterns surface before they become losses.

Problem

Clinics bleed revenue to no-shows and admin overhead, and patients abandon booking flows that aren't on the channel they actually use.

Approach

We built an orchestration layer that unifies booking, intake, follow-ups, and refills across WhatsApp, voice, web, and email — with availability checks and automated reminders built in.

Outcome

Patients book on their preferred channel and reminders go out automatically, cutting no-shows and freeing front-desk staff from repetitive coordination.

Problem

D2C founders spend weeks writing content, can't pinpoint where their funnel leaks, and can't afford the agency retainers larger competitors rely on.

Approach

We built a growth toolkit that automates the highest-leverage tasks: keyword research and on-page SEO, multilingual bulk product descriptions, GA4 funnel analysis, and review-driven product signals.

Outcome

Catalogs that would take weeks to optimize ship in days, and funnel drop-offs become visible and fixable without growing headcount.

Problem

Reviewing a single contract costs hours of associate time and still misses edge cases, while legal research stays slow, inconsistent, and expensive to scale.

Approach

We built a hierarchical parsing pipeline that maps clause relationships before extraction, flags risk against configurable playbooks with a mandatory rationale, and logs every decision to an append-only audit trail.

Outcome

Contracts are reviewed in a fraction of the time with consistent risk flagging and a defensible, fully auditable record of every AI decision.

Problem

Bookkeeping for Indian SMBs means manually reconciling invoices and preparing GST returns — slow, error-prone work that doesn't scale.

Approach

We built an OCR pipeline that extracts GSTIN, HSN codes, and GST breakdowns from any invoice quality, auto-categorizes expenses, reconciles bank statements, and pre-populates ready-to-file returns.

Outcome

Most invoices are processed without human intervention and GST filing becomes a review-and-submit step instead of a multi-day exercise.

Problem

Small manufacturers can't afford enterprise vision systems, and human QA misses subtle defects while producing none of the audit trails ISO compliance requires.

Approach

We built a transfer-learning pipeline that trains on as few as 200 images per defect class, integrates with the production line, triggers work orders on threshold breaches, and generates ISO-compliant audit trails.

Outcome

Production-grade defect detection stands up in weeks rather than quarters, catching issues human inspectors miss without an enterprise budget.

Problem

Teams ship software validated against a handful of happy-path tests, then discover failure modes only when real users hit them in production.

Approach

We built an engine that generates diverse personas across demographics, simulates realistic behavior (including mistakes), and runs multi-channel stress tests at scale to surface failure modes before launch.

Outcome

Every Keyved platform is stress-tested by thousands of synthetic users before a real client touches it — friction gets found and fixed early.

Problem

Data ingestion breaks silently — schema drift, missing fields, upstream API changes — and teams don't notice until analytics are corrupted weeks later.

Approach

We built a pipeline with observability and self-correction built in: it ingests from multiple sources, validates schema, flags anomalies, attempts automated remediation, and escalates to human review when it can't self-heal.

Outcome

Pipelines catch and fix issues in real time instead of failing silently, with a clear record of what was auto-remediated and what needed human review.

Problem

Generic LLMs know everything but understand nothing about a company's data, workflows, and permissions — they can't answer "what's my Q3 revenue by region" or "which customers have unpaid invoices over 60 days."

Approach

Enterprises onboard, define role-based access rules, and connect data sources and internal tools. The system layers RAG, tool orchestration, code execution, and tag-based scoping on top — so a CEO sees all data while a salesperson sees only what their role permits.

Outcome

Teams get an internal Claude that knows their numbers, people, and workflows — answering complex queries with step-by-step reasoning, all scoped to user role and fully audited.