Our Services
Production AI Problems. One Diagnostic.
We've shipped AI in production — hospitality, health tech, fintech. We know where it breaks after it ships: the margin bleed, the RAG failures, the enterprise deals stalling on security questions. We diagnose first, then build.
Your AI Features Are Generating Revenue for AWS, Not You
You've shipped AI features. Users love them. You're charging the same flat price you charged before AI existed. Some users send 3 requests a day. Others send 300. You have no idea which customers are profitable on AI and which ones you're subsidising. The metering doesn't exist. The entitlements aren't configured. The billing tiers haven't changed since the features launched. Most mid-market SaaS companies leave six figures of annual revenue uncaptured because the billing infrastructure hasn't kept pace with the product.
What we build
Usage Metering & Entitlements
Wire per-request, per-user, or per-feature metering events from your AI features into your billing system. Configure tier-based entitlements — who gets which model, at what rate, up to what limit.
Billing Tool Selection & Integration
Evaluate and implement the right tool for your stack: Stigg, Schematic, Flexprice, or Stripe native. We know the trade-offs cold — runtime enforcement, credit wallets, BYOK support, Stripe dependency.
Customer-Facing Usage Dashboards
Build the usage visibility your enterprise customers are asking for. Current consumption, remaining credits, feature access by tier — so your customers stop asking your support team.
AI Monetization Audit
Map every AI feature to its infrastructure cost and current pricing. Identify where revenue is leaking. Model what happens to margin and revenue under different tier structures and metering approaches.
Our approach
We start with the audit before we recommend a tool. The billing architecture that's right for a $15M SaaS with 3 AI features is different from one with 12. Most teams pick Stripe and build something custom that turns into a maintenance burden. We've seen enough of these to know which corner to start from.
How to start
Free 30-min Call
We show you the metering and entitlements layer of our live AI platform — what per-tier model gating looks like in practice.
AI Monetization Audit ($5K–$8K, 2 weeks)
Written Monetization Readiness Report: every AI feature mapped to cost and current pricing, revenue leakage identified, tool recommendation with rationale, implementation roadmap.
Build ($15K–$35K, 4–8 weeks)
Metering, entitlements, and billing infrastructure implemented. Everything wired, tested, and documented. Your billing infrastructure catches up to your product.
Which Customers Are You Subsidising With AI Features They Don't Pay For?
Most CTOs can tell you their total monthly OpenAI or Bedrock bill. Almost none can tell you which customer or pricing tier is generating AI revenue vs. absorbing AI cost. Your billing platform shows what you charged. Your LLM gateway shows what you spent on inference. Nobody connects the two at the customer level. Without that connection, pricing decisions are guesswork, your Series B investors will ask questions you can't answer, and the next product launch will repeat the same mistake.
What we build
Per-Customer AI Cost Attribution
Instrument your AI pipeline with Langfuse for per-request, per-user, per-feature cost tracking. Join inference cost data to Stripe billing data. Produce the per-customer AI P&L that nobody in your company has seen.
Per-Feature Margin Analysis
Which AI features are margin-positive? Which are bleeding money? Which usage patterns are anomalies vs. normal? We break it down by feature, customer segment, and usage tier.
Pricing Scenario Modelling
Model what happens to margin under 2–3 pricing restructures: credit-based billing, usage caps, model tier gating, or price adjustments. Specific projections, not vague directional guidance.
Ongoing Margin Monitoring
Margin dashboards and anomaly alerts so cost spikes get caught before the end of the quarter. Continuous visibility, not a one-time report.
Our approach
The tools to solve this already exist — Langfuse for LLM observability, CloudZero for cloud unit economics. What a $20M SaaS doesn't have is a FinOps team to evaluate, select, implement, and join the data together at the customer level. That's what the diagnostic produces: the per-customer P&L, the anomaly flags, and the specific pricing scenarios with projected margin impact.
How to start
Free 30-min Call
We show you what per-customer cost attribution looks like in a live AI system — the margin map that most CTOs are missing.
AI Margin Diagnosis ($8K–$13K, 2–3 weeks)
AI Margin Intelligence Report: per-customer cost-to-serve, per-feature cost breakdown, usage pattern analysis, pricing simulation, anomaly flags. The number nobody in your company has today.
Build ($15K–$30K, 4–8 weeks)
Model routing implementation, billing integration, ongoing margin dashboards, anomaly alerts. The cost visibility layer built into your production pipeline.
Your RAG Is Live. Your Users Are Complaining. We Fix Production RAG.
Hundreds of SaaS companies shipped RAG features in 2024–25. Most shipped the happy path. Now users are complaining about wrong answers, irrelevant results, or confident-sounding nonsense. The team that built the prototype can't diagnose it at scale. The problem usually isn't the model — it's the chunking strategy that made sense in testing and breaks on real documents, the embedding model that worked on 100 docs and degrades on 10,000, the retrieval pipeline with no quality monitoring. These problems compound quietly until churn starts to show.
What we build
RAG Quality Audit
Measure what you actually have: hallucination rate, retrieval relevance, coverage gaps, latency per query type. Root cause analysis across chunking, embedding model choice, vector database configuration, and guardrails.
Retrieval Pipeline Fixes
Re-chunking with improved strategies. Embedding model evaluation and migration where needed. Hybrid search implementation (semantic + keyword) where pure semantic retrieval is failing.
Guardrails & Quality Monitoring
Citation checking, confidence thresholds, human escalation triggers. Caching for repeated queries. Ongoing quality monitoring so you know when retrieval quality drifts before users do.
Latency Optimisation
Query-level latency profiling, bottleneck identification, caching strategy, infrastructure right-sizing. Fast RAG that's also accurate.
Our approach
We've built production RAG from inside a startup and know where it breaks at scale. The diagnosis comes before the fix — most RAG problems have a specific root cause that's faster to fix once identified than to throw general improvements at. We run the audit first, surface the root causes, and then implement the fixes that will actually move the numbers.
How to start
Free 30-min Call
We show you quality and latency monitoring in a live RAG system — what the instrumentation looks like and what it surfaces.
RAG Audit ($5K–$8K, 1–2 weeks)
RAG Health Report: hallucination rate baseline, retrieval relevance scoring, root cause analysis, fix prioritisation with effort and impact estimates.
Remediation ($15K–$30K, 3–6 weeks)
Re-chunking, hybrid search, guardrails, caching, quality monitoring. Production RAG that works at the scale you actually have.
The Enterprise Deal That Stalled on an AI Security Question You Couldn't Answer
Enterprise buyers are starting to ask: "What's your AI security posture?" Most $5–50M SaaS companies don't have an answer. The attack surface is real — prompt injection that leaks other customers' data, context window manipulation that bypasses access controls, agent tool-call vulnerabilities, output that exposes PII. Most companies know this needs addressing. Nobody has done the assessment. The enterprise deal waits.
What we build
Prompt Injection Testing
Systematic testing for prompt injection vulnerabilities — direct injection via user inputs, indirect injection via documents or tool outputs. Documented findings you can share with security-conscious prospects.
Data Leakage Vector Assessment
Can your AI surface expose one customer's data to another through the context window? Can it be manipulated into revealing training data or system prompts? We find the vectors before adversarial users do.
Agent & Tool-Call Security Review
For agent-based systems: authorization boundary review, tool-call permission analysis, sandboxing assessment. Agentic systems have a fundamentally different attack surface than single-turn AI.
Security Hardening Implementation
Input validation and sanitisation, output filtering and PII detection, sandboxed agent execution, audit trail implementation, rate limiting, security monitoring. Aligned with OWASP LLM Top 10.
Our approach
The diagnostic produces a customer-shareable AI security summary you can hand to enterprise prospects before they ask. Azmi brings AWS security architecture — IAM, KMS, WAF, VPC — so the implementation goes beyond application-layer configuration. We've built AI in regulated contexts (fintech, health tech, hospitality) and know what enterprise security scrutiny looks like from the inside.
How to start
Free 30-min Call
We walk through the attack surface of our live agentic commerce system — what the security boundaries look like and where the gaps typically are.
AI Security Quickscan ($4K–$7K, 1–2 weeks)
Customer-shareable AI security summary + prioritised hardening roadmap. The document your enterprise prospects are asking for.
Hardening ($12K–$25K, 3–5 weeks)
Input validation, output filtering, agent sandboxing, audit trail, rate limiting, security monitoring. Production-grade AI security aligned with OWASP LLM Top 10.
Production AI Feature in 6 Weeks. Not a Prototype.
The board wants AI on the roadmap. Your team built a demo 6 months ago. It never shipped — too many dependencies, too much uncertainty, too easy to defer. Or you haven't started and the Q3 deadline is looking real. You need to go from "we want AI" to "it's live, metered, and enterprise-ready" in one quarter with a team that handles the whole thing: AI implementation, AWS infrastructure, billing integration, security guardrails. One vendor. One invoice. No coordination overhead.
What we build
Scoping Sprint
One week. Define the feature, architect the solution, estimate infrastructure costs, plan the 4–6 week build timeline. You get an architecture document and a clear statement of what ships and what gets deferred.
AI Feature Build
AI search, support automation, document analysis, or conversational product discovery — one scoped feature shipped to production. Includes the AI layer, AWS infrastructure, and integration with your existing product.
Metering & Billing Integration
Billing infrastructure built in from day one — not retrofitted later. The feature ships already wired to metering so you can charge for it.
Security Guardrails
Input validation, output filtering, rate limiting, audit logging — the minimum viable security layer that won't embarrass you in an enterprise security review.
Our approach
We've shipped AI with a deadline and real users. We know what the 6-week constraint forces you to prioritise and what you can safely defer. The scope is fixed. The timeline is fixed. What's flexible is which feature you ship — and we'll tell you honestly which one is achievable in 6 weeks and which one isn't.
How to start
Free 30-min Call
We show you a complete AI system we shipped from architecture to production — the full stack, not just the AI layer.
Scoping Sprint ($5K, 1 week)
Feature architecture, AWS infrastructure plan, cost estimate (infrastructure + ongoing inference), and a realistic 4–6 week build timeline.
Build Sprint ($20K–$40K, 4–6 weeks)
One production AI feature shipped, deployed, documented, and handed off. Includes metering integration and security guardrails.
Why Our AI Work Holds Up Where Pure AI Shops Fail
Every AI problem has an infrastructure layer — cost attribution needs CloudWatch, security hardening needs VPC isolation, and shipping in 6 weeks means infrastructure as code, not a script on someone's laptop. We've been building on AWS for over a decade. Pure AI shops hand you a working demo. We hand you a deployed, metered, monitored system that won't surprise you at 3 AM.
AWS Well-Architected reviews, cloud architecture, cost optimization, and legacy modernization are also available as standalone engagements. Book a call to discuss →
How we work
How Every Engagement Works
Discovery Call
Free, 30 minutes
Tell us what you’re dealing with. We ask diagnostic questions, show you something relevant, and give honest feedback. If we’re not the right fit, we say so.
Focused Diagnostic
$4K–$13K, 1–3 weeks
Scoped to your specific problem. You get a written report with findings and a clear implementation roadmap. Fixed scope, fixed timeline, one deliverable.
Build & Deploy
Scoped per project
Build, deploy on AWS, stabilize, hand off with documentation. Infrastructure as code. Monitoring from day one.
Ongoing Support
Optional
Continued optimization, model updates, cost monitoring, feature work. Some clients keep us on retainer. Happy to.
Which of These Problems Are You Dealing With?
Tell us what’s happening. We’ll tell you which diagnostic fits — or whether you need one at all. 30 minutes, straight feedback.