Case Studies

Real clients. Real numbers.

Four engagements from the past 18 months. Every metric is measured against the client's pre-engagement baseline and verified by their analytics team. Tech tags reflect the actual production stack — not what we wanted to use, what we shipped.

🏦 FinTech · B2B2024 · 14 weeks
Next.js 16PythonFastAPIPostgreSQLRedisDockerAWSTerraform

Real-time Payments API — p99 latency cut 72%

A Series-C fintech processing $4.2B/yr in B2B payments needed to bring p99 API latency under 200ms to qualify for tier-1 bank partnerships. We rebuilt the request hot-path, replaced synchronous compliance calls with an event-driven sidecar, and migrated to a multi-region active-active Postgres+Redis topology.

P99 Latency
720ms200ms
-72%
Throughput
8K rps22K rps
+175%
Infra Cost / Mo
$48K$31K
-35%
🛒 E-commerce · D2C2024 · 10 weeks
Next.js 16PythonOpenAILangGraphPineconePostgreSQLVercel

AI Shopping Assistant — +38% checkout conversion

A 9-figure D2C skincare brand deployed a RAG-grounded shopping assistant across PDP pages and cart. We built the retrieval pipeline over 14K SKUs + 280K reviews, integrated a multi-agent orchestrator for product-match + ingredient-safety checks, and A/B-tested against the static chatbot for 8 weeks.

Checkout CVR
2.6%3.6%
+38%
Avg. Order Value
$42$54
+29%
Returns Rate
12.4%8.1%
-35%
🏥 Healthcare · HIPAA2023 · 22 weeks
PythonFastAPIWeaviateOpenAIBGE-RerankerPostgreSQLDockerGCP

Clinical Notes RAG — $1.4M annual cloud savings

A regional hospital network (1,200 beds, 14 facilities) needed to make 18M anonymized clinical notes searchable for clinical research. We built a hybrid RAG pipeline with per-patient ACLs, replaced a $2.4M/yr third-party search contract with a self-hosted stack, and gave researchers sub-second retrieval across the full corpus.

Annual Cloud Spend
$2.4M$1.0M
-$1.4M / yr
Retrieval P99
4.8s112ms
-98%
Researcher NPS
+8+72
+64 pts
🚚 Logistics · Enterprise2024 · 18 weeks
PythonLangGraphAnthropicPostgreSQLRedisKafkaKubernetesAWS

Fleet Orchestration Agents — 31% fewer empty miles

A national last-mile logistics operator (1,800 trucks, 40 hubs) needed to reduce deadhead miles. We deployed a multi-agent orchestrator that runs every 90 seconds: a forecast agent predicts demand, a matcher agent proposes load pairings, a validator agent checks DOT compliance, and a human-in-loop dispatcher approves exceptions.

Empty Miles
21.4%14.8%
-31%
Fuel Spend / Mo
$2.8M$2.1M
-25%
On-Time Delivery
91.2%96.4%
+5.2 pts

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