Systems We've Built.
Results We've Delivered.

Order-status questions, return policy, and basic product queries made up the bulk of inbound support volume, with repetitive, answerable questions that still needed a human to look up an order and type a response, which meant slower replies during peak periods precisely when quick answers mattered most for conversion and repeat purchase.

An AI support agent grounded directly against live order data, not a static FAQ bot, that resolves order-status, returns, and policy questions in real time and escalates to a human agent for anything outside its scope instead of guessing. Support volume that used to queue behind a human now resolves immediately for the repetitive majority, freeing the human team for the cases that actually need judgment.

Prospective buyer and tenant inquiries arrived almost entirely through WhatsApp — the channel people actually used, but nothing structured caught them. Leads sat inside chat threads with no record, follow-ups depended on an agent remembering to circle back, and matching an inquiry to the right property in inventory was manual and error-prone once volume picked up.

A WhatsApp-native CRM that captures every inbound property inquiry directly from the chat, creates or updates a lead record automatically, and links it to the relevant listing in inventory. Follow-up reminders fire on a schedule instead of depending on an agent's memory, and inquiries route to the right agent based on property and workload, all without asking buyers or tenants to leave the channel they were already using.

Prompt and model changes across our own AI products were shipping on manual review, the exact gap we later wrote about publicly. There was no self-hosted, CI-integrated way to compare a change against an explicitly approved baseline and block a regression before it merged, and every eval platform evaluated offered that only behind an enterprise-only self-hosting tier.

A self-hosted evaluation and regression platform: LLM-as-judge and lexical scoring, explicit baseline comparison instead of a drifting rolling average, and severity-aware CI exit codes that actually gate a merge. The detection and regression engine was open-sourced separately (Apache-2.0) so the core logic is auditable independent of the hosted platform, and the platform itself now runs the same evals-in-CI discipline behind features like EvalCI.

Data pipelines that combine multiple processing stages: ingestion, transformation, validation, enrichment, typically hand each stage to a fixed script with no ability to reason about malformed input, adapt to schema drift, or explain why a given row failed validation. When something breaks, the on-call engineer is the fallback reasoning layer.

A multi-agent pipeline where specialized agents handle ingestion, transformation, and validation stages independently, coordinated through LangGraph with each agent able to reason about ambiguous or malformed input rather than hard-failing the whole run. Built with full architecture documentation and an interactive diagram from day one, and backed by 511 tests at 84% coverage before being considered production-ready.

A multi-tenant SaaS platform needed real financial infrastructure behind its billing, not just a payment gateway bolted onto a database. Without a proper accounting model, revenue recognition, refunds, and reconciliation against actual payment provider settlements were being handled ad hoc, making the numbers finance and engineering each trusted diverge more with every billing cycle.

A double-entry ledger built on an 80-account, Nigerian-compliant chart of accounts, with six billing adapters for different payment and subscription flows and native Paystack integration. Every transaction: subscription charge, refund, proration, provider fee, posts as a balanced ledger entry, so the books reconcile by construction instead of by month-end cleanup.

A multi-outlet pharmacy chain relied on manual inventory planning, leading to frequent stockouts of essential medicines, excess inventory, product expiries, and difficulty predicting seasonal demand fluctuations.

An AI-powered demand forecasting platform that analyzes sales history, inventory movements, seasonal trends, weather patterns, public holidays, and health indicators to generate accurate demand predictions and automated replenishment recommendations.

A growing fashion design business relied on paper measurement books to manage customer records, making it difficult to retrieve measurements quickly, track historical changes, and scale operations as the customer base expanded.

A centralized digital measurement management platform that enables staff to register customers, capture body measurements, instantly retrieve records, and maintain a complete measurement history for future fittings and alterations.

A professional services firm was spending 15+ hours per week manually extracting data from contracts, invoices, and compliance documents. With inconsistent output quality and high error rates on structured fields.

A production-grade autonomous extraction engine that processes PDFs, scanned images, and contracts into validated, structured JSON. With a multi-stage pipeline covering OCR, NLP extraction, schema validation, and confidence scoring.

Development teams building LLM-powered applications had no visibility into prompt performance, token costs, latency distributions, or failure rates. Making debugging and optimisation guesswork.

A full-stack observability platform with an OpenTelemetry-compatible tracing SDK, high-throughput ingest pipeline, and a real-time React dashboard. Providing Datadog-level visibility for LLM applications.

A growing retail business was managing inventory, orders, and customer communication across WhatsApp, Excel sheets, and phone calls, with no system of record and constant data loss between handoffs.

A fully WhatsApp-native business operating system that lets owners and staff log sales, expenses, and customer debts by messaging naturally, with automatic daily business briefings, real-time inventory tracking, and automated debt-collection reminders. Requiring zero app downloads from staff or customers.