Reduce AWS CostsFix Backend Performance
AI-Assisted DeliverySystems Engineers in Control
Integrate AI IntoYour Existing Stack
Cloud Spend ClimbingWe Find the Waste-
Legacy Blocking DeploysServerless Done Right
Most scaling systems fail in the same ways
AWS costs outpace value
Monthly cloud spend grows with traffic while architecture stays unchanged. Waste hides in Lambda, API Gateway, data transfer—and increasingly in AI/inference usage.
APIs degrade under real load
Latency spikes and timeouts show up in production. Hot paths in Go services, queues, databases, and model calls need engineering—not more instances.
Legacy architecture slows shipping
Monoliths and manual ops block safe deploys. Teams need event-driven, serverless boundaries—and AI features that plug into those boundaries cleanly.
AI bolted on, not engineered in
Prototype chatbots and one-off scripts never become reliable product paths. Without queues, budgets, and observability, AI becomes another production liability.
Services
Productized backend engagements—clear scope, senior execution, AI-assisted delivery. No generic app-studio retainers.
AWS cost optimization audit
- Baseline spend and find waste in compute, Lambda, API Gateway, networking, and storage—including AI/inference workloads when they are on the bill
- AI-assisted inventory and pattern matching across accounts, reviewed by engineers before recommendations ship
- Prioritized savings plan with guardrails so gains hold as traffic and model usage grow
Backend performance engineering
- Trace production bottlenecks in APIs, workers, and AI call paths
- Tune Go services and distributed workflows under real load
- Improve latency and reliability—AI accelerates analysis; engineers own the fix
Serverless migration
- Design event-driven boundaries for the right workloads
- Move suitable surfaces to AWS Lambda with clear cutover plans
- AI-assisted refactor scaffolding and test gaps, with human review before production
AI systems integration
- Wire LLMs and inference into existing Go/Lambda/event-driven backends
- Design queues, retries, and timeouts so AI features do not block core product paths
- Cost and latency budgets for Bedrock and related AWS AI services—operated like the rest of your stack
AI that ships inside real systems
We use AI-assisted development to move faster on audits, refactors, and migrations—and we integrate AI capabilities into the backends you already run on AWS.
AI-assisted engineering (how we work)
Senior engineers stay in charge of architecture and production risk. AI accelerates the work around them:
- Faster codebase reconnaissance across Go services, Lambdas, and IaC
- Assisted profiling notes, test scaffolding, and migration diffs—reviewed before merge
- Shorter cycles from diagnosis to a deployable change set your team can own
AI integration into your stack
When you need AI features in production, we wire them into existing systems—not a disconnected demo:
- LLM and inference paths on AWS (Bedrock, Lambda, queues, API Gateway)
- Event-driven pipelines that call models without blocking core APIs
- Cost, latency, and reliability controls so AI workloads do not surprise the bill or the SLO
Same standard as the rest of our practice: production-safe design, measurable outcomes, and handoff your platform team can operate.
We work with
- SaaS companies moving from MVP to serious growth
- Engineering teams seeing AWS bills rise faster than revenue—including AI/inference spend
- Startups where APIs or workers fail under production traffic
- CTOs modernizing legacy backends toward Lambda, event-driven design, and production AI
- Product and platform leads who want AI features inside existing systems—not a parallel stack
Outcomes we target
Engineering-led, AI-assisted engagements with measurable infrastructure results.
20–40% AWS cost reduction
After audit and remediation: rightsizing, Lambda and API Gateway tuning, caching, and cutting redundant spend—including runaway AI/inference costs when they are in scope.
2x–5x faster API paths
Latency improvements from profiling Go services, fixing IO patterns, queues, concurrency—and keeping AI call paths off the critical request when they do not belong there.
Stability under production load
Fewer incidents from timeouts and overload when backpressure, limits, and failure domains are designed in—for core APIs and AI-backed features alike.
Faster delivery with AI-assisted engineering
Shorter cycles from diagnosis to shipped change: AI accelerates exploration and scaffolding; senior engineers own architecture, review, and production risk.
Clients
Organizations we have supported with AWS, serverless, backend APIs, and production systems engineering.
Representative case studies
Anonymized examples reflecting common engagement patterns. Your architecture and numbers will differ; the approach is the same.
B2B SaaS — runaway Lambda + AI spend
Problem. Monthly AWS spend jumped 3x after a traffic milestone. Lambda concurrency, API Gateway, outbound data, and a new inference path dominated; no single owner for cost.
Solution. Cost audit with AI-assisted inventory across accounts, concurrency and memory tuning, API caching, async offload for model calls, and removal of redundant cross-region traffic.
Result. ~32% infrastructure cost reduction within one billing cycle; p95 API latency improved ~40% from fewer duplicate and blocking AI calls.
Growth-stage API — timeouts at peak
Problem. Core Go service looked fine in staging but 5xx and tail latency spiked under production concurrency—including synchronous calls to an LLM on hot paths.
Solution. Production profiling, query and batching fixes, bounded worker pools, and queue-backed AI offload so inference no longer sat on the request path.
Result. p99 latency ~60% lower at peak; error rate dropped below prior SLO; team gained a repeatable load profile for releases.
Legacy monolith — AI feature stuck in a prototype
Problem. Single deployable blocked feature teams; an AI prototype lived outside the main system and could not meet reliability or cost expectations.
Solution. Strangled high-churn domains into Lambda handlers and event buses; wired the AI path into queues with budgets and observability alongside the transactional core.
Result. Deploy frequency moved from bi-weekly to multiple times per week for migrated surfaces; AI feature reached production with predictable cost and on-call ownership.
About Ciphergram
Ciphergram is a backend systems consultancy founded by engineers who specialize in Go, AWS, distributed systems, and AI-assisted delivery. We focus on infrastructure-level problems that affect cost, performance, and scalability—and on integrating AI into those systems so it behaves like production software, not a side experiment.
We work the way your CTO or platform lead would want: narrow scope, explicit tradeoffs, AI where it speeds diagnosis and implementation, and recommendations you can implement or hand to your team with confidence.
Why technical leaders bring us in
- Deep Go backend engineering for services, workers, and high-throughput paths
- Hands-on AWS and Lambda experience—including AI/inference workloads on Bedrock and related services
- AI-assisted engineering that accelerates audits and refactors without skipping review or ops discipline
- Systems architecture spanning event-driven design, reliability, cost-aware scaling, and production AI integration
Since 2014 we have shipped and advised on production systems. Today the practice is focused: AWS cost optimization, backend performance engineering, serverless migration, and AI systems integration for SaaS and scaling startups.
When cost, latency, or AI in production breaks, you need systems engineers—not a generic AI demo shop
Tell us about your stack, AWS footprint, AI usage, and what production is doing under load. We respond with a scoped review or audit plan.
Engagements
Scoping starts with your stack, traffic patterns, AI usage, and business constraints. Below are typical shapes of work—not shopping-cart SKUs.
Cost optimization audit
- Billing and resource inventory across AWS accounts
- Waste analysis: Lambda, API Gateway, data transfer, storage, and AI/inference spend
- AI-assisted pattern finding across configs and usage—engineer-reviewed recommendations
- Prioritized remediation list with expected savings bands
Performance engineering sprint
- Production profiling and bottleneck analysis
- Go service, distributed systems, and AI call-path tuning
- Load validation against agreed scenarios
- Runbooks and monitoring gaps called out explicitly
Serverless migration program
- Domain boundaries and event design
- Lambda, queues, and integration patterns
- AI-assisted scaffolding for handlers and tests; humans own cutover risk
- Knowledge transfer for your platform team
AI systems integration
- Integrate LLMs into existing backends (Go, Lambda, event buses)
- Design async inference paths, retries, and failure isolation
- Cost and latency budgets for Bedrock and related AWS AI services
- Observability so AI features are operable by your on-call team
If you are unsure where to start, we begin with a short technical review and point you to the right engagement.
Request a backend systems reviewFrequently asked questions
What does Ciphergram do?
Ciphergram is a specialist backend systems consultancy. We help SaaS companies reduce AWS costs, improve backend performance, modernize with serverless architecture, and integrate AI into existing production systems—using Go, AWS Lambda, and AI-assisted engineering.
How do you use AI in your work?
Two ways. First, AI-assisted delivery: we use AI to accelerate codebase analysis, scaffolding, and migration diffs while senior engineers own architecture, review, and production risk. Second, AI systems integration: we wire LLMs and inference into your existing AWS backends with cost, latency, and reliability controls.
Who do you work best with?
SaaS teams scaling past MVP, engineering organizations with rising AWS or AI bills, startups seeing production performance issues, and CTOs adding production AI to legacy or serverless backends.
What is an AWS cost optimization audit?
A fixed-scope review of how your AWS bill maps to workloads: Lambda, API Gateway, compute, networking, storage, and AI/inference spend when in scope. You get a prioritized list of changes with expected savings and risk notes.
How do engagements begin?
Most teams start with a backend systems review or a cost and performance audit. We clarify scope, access needs, AI usage, and success metrics before work starts.
Request a backend systems review
Share your stack, symptoms, AWS context, and any AI features in flight. We reply with next steps for a cost analysis, performance audit, serverless migration, or AI integration assessment—usually within one business day.
CONTACT US
ADDRESS
20 North Orange Avenue, Unit 1100
Orlando, Florida 32801
CALL US
1-708-517-9987