That holds up under real load — measured before, proven after.
We make slow web applications fast and keep them fast under traffic. We baseline what you have, find the real bottleneck instead of guessing, and show you the before-and-after numbers.
Fixed scope, one accountable lead. Most engagements reach a measured result in 4–8 weeks.
Because slowness is rarely everywhere — it lives in a few specific places, and a rewrite throws out the 90% that works to chase the 10% that drags. Teams rebuild, ship, and find the new stack is slow too, because the bottleneck was never the framework. It was an unindexed query, an N+1 pattern, a render-blocking bundle, or a chatty API that no rewrite addresses by accident.
The cost of leaving it is not abstract. Slowness silently bleeds revenue, search ranking, and cloud spend at once.
Web application performance optimization done right is a targeted operation — measure, find the real bottleneck, fix it, prove the gain — not a gamble on a clean slate.
Performance work is not one knob. It is a set of targeted interventions, each tied to a metric a buyer can read. For each: what it does, the benefit it produces, and how that plays out.
Tunes the three metrics Google measures on real users — how fast the main content paints, how quickly the page responds to a tap, and whether the layout jumps. Benefit — better real-user experience and a healthier search position. Passing thresholds lowers bounce and removes a ranking handicap.
For example, a product page whose Largest Contentful Paint drops from 4 seconds to under Google’s 2.5-second target stops losing the shopper who would have abandoned mid-load — and clears a known SEO penalty in the same pass.
Finds the unindexed lookups, N+1 query patterns, and table scans that dominate response time, and fixes them at the data layer. Benefit — lower latency on the slowest transactions, with no rewrite. The pages that hang the worst are usually the ones doing the most database work.
For example, a dashboard that took 6 seconds because one endpoint fired hundreds of small queries returns in under a second once the access pattern is fixed — without touching the rest of the app.
Profiles your service calls, adds the right caching, trims payloads, and removes chatty round-trips between services. Benefit — faster responses and more headroom under load.
For example, a checkout API that made eight sequential downstream calls is collapsed and cached so the step that gated every purchase responds in a fraction of the time — and survives the traffic spike that used to time it out.
Cuts render-blocking JavaScript, splits and defers bundles, optimizes images, and fixes the layout shifts that hurt INP and CLS. Benefit — pages that feel instant and interactive sooner.
For example, deferring a heavy third-party script moves a marketing page’s interactivity forward by seconds, so a visitor can tap “Buy” the moment they decide instead of waiting for the page to settle.
Moves static and cacheable content to the edge and sets cache and compression policy correctly. Benefit — faster global delivery and less origin load.
For example, serving assets from the edge cuts time-to-first-byte for distant users and pulls repetitive traffic off your servers — faster pages and a smaller bill at once.
Models realistic peak traffic, finds where the system breaks before customers do, and engineers the headroom to absorb it. Benefit — confidence that the busiest day won’t take the site down.
For example, a retailer load-tests its Black Friday peak in advance, finds the database connection pool saturates at 3× normal traffic, and fixes it in October instead of during the outage.
Slowness hides in layers, so the work spans the stack. Each item below is a measured intervention, not a vibe.
We instrument real-user monitoring and synthetic tests, capture a baseline, and locate where time actually goes — front-end, API, application code, or database — before changing a line. The AI-assisted Aegis AI review reads the code and traces to surface anti-patterns a manual pass would miss.
We tune LCP, INP, and CLS against Google’s thresholds (LCP under 2.5s, INP under 200ms, CLS under 0.1) using your real field data, not just a lab score — so the gain shows up for actual users and in Search Console.
We profile the slow queries, add or correct indexes, eliminate N+1 patterns, and tune the access layer — the highest-leverage fixes on most enterprise apps, and the ones a rewrite would never have addressed.
We cut latency in service calls, set caching and compression policy correctly, and push cacheable content to the edge — faster responses and lower origin load and cloud cost together.
We model realistic peak traffic, find the breaking point, and engineer the headroom — connection pools, autoscaling, query concurrency — so the busy day is boring. This pairs naturally with our DevOps services.
We leave the monitoring and load-test harness in place, instrumented for regression, and train your team to read it — so performance is maintained, not re-bought next year as part of application maintenance and support.
What you get when you hire us — all assigned to you
Six steps, one measured loop — the same delivery discipline behind all our software re-engineering work, focused on speed and scale. One accountable lead, fixed scope, no handoffs.
Instrument real-user and synthetic monitoring, capture today’s numbers and the targets we’ll be judged against.
Output: a documented starting point
Put AI on the code, logs, and traces to find where time actually goes across the stack.
Output: a ranked bottleneck list
Confirm the root cause of each bottleneck, not the symptom.
Output: a fix plan ordered by impact-per-effort
Engineers implement the fixes — queries, indexes, render path, caching, API shape — inside your environment.
Output: the optimized changes
Model peak traffic and prove the fix holds under it.
Output: a validated scalability ceiling
Re-measure against the baseline and report the before-and-after.
Output: the proven gain, monitoring left running
Most engagements reach a measured result in 4–8 weeks, with payment tied to ROI and full work-for-hire IP assignment signed at kickoff.
The hardest version of performance optimization is not a one-time speed-up; it is keeping an application fast across more than a decade of growth while it stays live the whole time. That is the work we have done on Bridge Athletic since 2012 — carrying a sports-tech platform through repeated rounds of re-platforming, code re-engineering, and performance optimization, paying down the debt that slows a system down each pass, with the product never going dark.
It grew into the platform now used by USC, the LA Rams, and MLB and MLS teams — the kind of load that punishes a slow application, sustained for 12+ years.
That is the difference between optimizing a page and engineering for durable performance: the second one is what we do. The same delivery discipline holds a 200+ location restaurant business — BJ’s Restaurants — at twice-a-week releases with zero critical defects across four years, because performance you can’t measure and protect doesn’t stay.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. We baseline first and report the numbers because a performance claim you can’t see is one you shouldn’t pay for.
We find it before we fix it. AI reads your code, logs, and traces to locate the real bottleneck; our engineers fix it. No rewrite-by-reflex, no guessing — the diagnosis is measured and the fix is targeted.
Proven before and after. We baseline first and report the gain against your own metrics. A performance result you can’t see on a dashboard isn’t a result.
Built to stay fast. We leave the monitoring and load-test harness in your hands and train your team — so the gain holds instead of decaying back to slow.
Founder-led, one accountable lead. No account managers, no handoffs — the person who scopes it answers for the numbers it produces.
Built to transfer. The optimized system, the harness, and the findings are assigned to you under full work-for-hire IP assignment.
Where every 100ms maps directly to conversion and basket size; we tune Core Web Vitals and checkout latency against live catalog and order load. Ecommerce software →
Dashboards and APIs that must stay fast as customers and data grow; query tuning and scalability engineering carry the load. Software re-engineering →
Applications a decade into production where accumulated technical debt has quietly throttled speed; we pay it down without downtime as part of application modernization.
What teams want to know before optimizing a web application.
It covers the whole stack where slowness hides: Core Web Vitals (LCP, INP, CLS), database and query tuning, API and backend latency, front-end render performance, CDN and caching, and load-tested scalability engineering. We baseline your real numbers first, find the actual bottleneck rather than guessing, fix it, and report the before-and-after — so the scope is whatever is genuinely slowing you down, in priority order.
AI reads what humans can’t scan at scale — your full codebase, logs, and traces — to surface the performance anti-patterns (N+1 queries, render-blocking work, hot paths) that a manual review would take weeks to find or miss entirely. It accelerates the diagnosis; our engineers own the fix. The AI finds it, people fix it, and every change is verified against the baseline.
Yes. Core Web Vitals — LCP, INP, and CLS — are a confirmed part of Google’s page-experience ranking signals (Google for Developers), so improving them removes a known handicap. Speed also lifts conversion directly: a Deloitte and Google study found a 0.1-second mobile speed gain raised retail conversion by 8.4% (web.dev). The same work helps users and rankings at once.
Often, yes — though we don’t promise a specific number without measuring first. Removing N+1 queries, right-sizing caching, fixing inefficient access patterns, and pushing cacheable traffic to the edge all cut the compute and bandwidth a request consumes, which lowers the bill alongside the latency. We report the resource change against the baseline so any cost saving is one you can see, not one we assert.
Both. Front-end tuning makes a single page fast; load testing proves the system stays fast under real peak traffic. We model your busiest realistic day, find where it breaks — saturated connection pools, query concurrency, autoscaling limits — and engineer the headroom before customers hit it, rather than discovering the ceiling during an outage.
We instrument real-user monitoring and synthetic tests to capture a baseline before any change, agree the target metrics at kickoff, and re-measure against that baseline at the end. You get a before-and-after on the numbers that matter to your business — Core Web Vitals, latency percentiles, throughput, error rate under load — and the monitoring stays running so regressions show up immediately.
You do — completely. The optimized application, the monitoring and load-test harness, and the findings transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to keep it fast. Most engagements reach a measured result in 4–8 weeks under a fixed-scope, ROI-tied model; our AI development cost guide gives real ranges.
Thirty minutes · No pitch deck
Bring the slow page, the timeout, or the traffic spike you’re dreading. We’ll explain how we’d baseline it, put AI on the code and logs to find the real bottleneck, and give you a measured path to an application that holds up under load.