11 Proven Ways to Optimize Website Performance

4 minutes, 23 seconds read

Published on Nov 26, 2019

Updated on Dec 18, 2019

Website performance optimization or simply, website optimization is a process of improving a website’s loading speed in the browser. It generally involves editing the website to optimize scripts, HTML, or CSS code and reducing the number of web page components like images, scripts, or video for faster loading.

What is web performance?

Web performance is the speed in which web pages are loaded and displayed on the user’s web browser.

The following are the website performance metrics-

#1 DNS lookup time

The Domain Name System (DNS) is the phonebook of the Internet. Users access online information through domain names, like www.mantralabsglobal.com. Web browsers interact through Internet Protocol (IP) addresses. DNS translates domain names to IP addresses so that browsers can load Internet resources.

#2 Initial connection

It is the time for a handshake between the browser and the server to retrieve the contents of the page. Handshaking is a process by which two devices initiate communications (here- browser and server). It initiates with the browser sending a message to the server indicating that it wants to establish a connection.

#3 Waiting time (TTFB)

It is the time spent waiting for the initial response, also known as the Time To First Byte. This time captures the latency (the delay between the instruction and data transfer) of a round trip to the server. It also accounts the time spent waiting for the server’s response.

#4 Download Time

It is the time spent receiving the response data.

11 Proven website performance optimization techniques

You’ll need to consider the following to enhance a website’s performance.

#1 Reduce DNS lookup time

Implement the following to reduce DNS lookup time-

Reduce the number of hostnames, that are used to generate a web page.
Host third party resources locally, which automatically reduces the DNS lookup.
Use DNS Cache, where cache time can be defined to different types of hosts, so it reduces the lookup time.
DNS prefetching: allows browsers to perform DNS lookup in the background while the user browses the current page.
Defer parsing Javascripts, which are not needed while loading a web page but render blockers.
Use a fast DNS provider: choose the DNS providers whose lookup time is minimal.

#2 Browser/Web cache

It is a temporary storage location on a computer for files that a browser downloads to display websites. Locally cached files may include any documents from a website, such as HTML files, CSS style sheets, JavaScript scripts, graphic images, and other multimedia content. When a user revisits the website, the browser checks for the updated content and downloads only those files or what is not already present in the cache. This reduces bandwidth usage on both the user and server-side and loads the page faster.

#3 Image Optimization

It is a process of delivering high-quality images in the right format, dimension, size, and resolution while keeping the smallest possible size. There are different ways to optimize images. You can resize, cache, or compress the image size.

#4 HTML, CSS, and JS Minification

While moving the source of website production, minify the contents of source code (Uglify), to reduce the overall size of the page. It will enhance the download speed for the page content on the web browser.

#5 HTML hierarchy

Maintain the standard HTML hierarchy, which means- push all the render-blocking scripts to the bottom of the page and keep only required assets on the header part of the load content. This way, the user doesn’t have to wait to see the actual page because of render-blocking scripts.

#6 Use Sprites

Sprite images are the group of images, which are combined to create a single image for a web page. As the number of server requests affects the bandwidth and loses the page speed score, it is better to combine all the possible images into sprite images.

#7 Enable compression

The web standards suggest GZIP compression. It is effective for optimum bandwidth utilization while rendering the contents. Let’s say- the overall size of the assets is 900KB. Enabling GZIP compression can compress the content size to at least 600KB. This enhances the bandwidth and pages render at a faster rate.

#8 Use secure channels/protocols

Prefer using secured channels to load the web page contents. It prevents the malware intro into the page.

#9 Reduce the number of redirections

Use a very less number of redirections in the websites. The introduction of too many redirections will consume the DNS lookup time and affect the page load time.

#10 Use CDN

Use CDN paths for the static resources, which enhances the load time performance of the website. CDN is useful for pre-caching static resources, which helps in reducing the time-to-index and hence reduces the load time. Also, distributed data centers host CDNs. Therefore, the nearest CDN host will fetch the assets- boosting the performance of the website.

#11 Avoid hotlinking

Hotlinking is the process of directly using the content from another site into the source website. Avoiding this will affect the bandwidth of both sites.

Also read – Everything you need to know about Test Automation as a Service.

why do we need webpage performance optimization

Do you have any questions regarding your website performance? Feel free to comment or write to us at hello@mantralabsglobal.com & stay tuned for our next article on 8 Factors that Affect Page Load Time & Website Optimization Strategies.

Generic AI is no longer enough. Domain-specific AI is the new enterprise advantage.

From hospitals to factories to insurance carriers, organizations are learning the hard way: horizontal AI platforms might be impressive, but they’re often blind to the realities of your industry.

Here’s the new playbook: intelligence that’s narrow, not general. Context-rich, not context-blind.
Welcome to the age of domain-specific AI agents— from underwriting co-pilots in insurance to care journey managers in hospitals.

Why Generalist LLMs Miss the Mark in Enterprise Use

Large language models (LLMs) like GPT or Claude are trained on the internet. That means they’re fluent in Wikipedia, Reddit, and research papers; basically, they are a jack-of-all-trades. But in high-stakes industries, that’s not good enough because they don’t speak insurance policy logic, ICD-10 coding, or assembly line telemetry.

This can lead to:

Hallucinations in compliance-heavy contexts
Poor integration with existing workflows
Generic insights instead of actionable outcomes

Generalist LLMs may misunderstand specific needs and lead to inefficiencies or even compliance risks. A generic co-pilot might just summarize emails or generate content. Whereas, a domain-trained AI agent can triage claims, recommend treatments, or optimize machine uptime. That’s a different league altogether.

What Makes an AI Agent “Domain-Specific”?

A domain-specific AI agent doesn’t just speak your language, it thinks in your logic—whether it’s insurance, healthcare, or manufacturing.

Here’s how:

Context-awareness: It understands what “premium waiver rider”, “policy terms,” or “legal regulations” mean in your world—not just the internet’s.
Structured vocabularies: It’s trained on your industry’s specific terms—using taxonomies, ontologies, and glossaries that a generic model wouldn’t know.
Domain data models: Instead of just web data, it learns from your labeled, often proprietary datasets. It can reason over industry-specific schemas, codes (like ICD in healthcare), or even sensor data in manufacturing.
Reinforcement feedback: It improves over time using real feedback—fine-tuned with user corrections, and audit logs.

Think of it as moving from a generalist intern to a veteran team member—one who’s trained just for your business.

Industry Examples: Domain Intelligence in Action

Insurance

AI agents are now co-pilots in underwriting, claims triage, and customer servicing. They:

Analyze complex policy documents
Apply rider logic across state-specific compliance rules
Highlight any inconsistencies or missing declarations

Healthcare

Clinical agents can:

Interpret clinical notes, ICD/CPT codes, and patient-specific test results.
Generate draft discharge summaries
Assist in care journey mapping or prior authorization

Manufacturing

Domain-trained models:

Translate sensor data into predictive maintenance alerts
Spot defects in supply chain inputs
Optimize plant floor workflows using real-time operational data

How to Build Domain Intelligence (And Not Just Buy It)

Domain-specific agents aren’t just “plug and play.” Here’s what it takes to build them right:

Domain-focused training datasets: Clean, labeled, proprietary documents, case logs.

Taxonomies & ontologies: Codify your internal knowledge systems and define relationships between domain concepts (e.g., policy → coverage → rider).
Reinforcement loops: Capture feedback from users (engineers, doctors, underwriters) and reinforce learning to refine output.
Control & Clarity: Ensure outputs are auditable and safe for decision-making

Choosing the Right Architecture: Wrapper or Ground-Up?

Not every use case needs to reinvent the wheel. Here’s how to evaluate your stack:

LLM Wrappers (e.g., LangChain, semantic RAG): Fast to prototype, good for lightweight tasks
Fine-tuned LLMs: Needed when the generic model misses nuance or accuracy
Custom-built frameworks: When performance, safety, and integration are mission-critical

Use Case	Reasoning
Customer-facing chatbot	Often low-stakes, fast-to-deploy use cases. Pre-trained LLMs with a wrapper (e.g., RAG, LangChain) usually suffice. No need for deep fine-tuning or custom infra.
Claims co-pilot (Insurance)	Requires understanding domain-specific logic and terminology, so fine-tuning improves reliability. Wrappers can help with speed.
Treatment recommendation (Healthcare)	High risk, domain-heavy use case. Needs fine-tuned clinical models and explainable custom frameworks (e.g., for FDA compliance).
Predictive maintenance (Manufacturing)	Relies on structured telemetry data. Requires specialized data pipelines, model monitoring, and custom ML frameworks. Not text-heavy, so general LLMs don’t help much.

Strategic Roadmap: From Pilot to Platform

Enterprises typically start with a pilot project—usually an internal tool. But scaling requires more than a PoC.

Here’s a simplified maturity model that most enterprises follow:

Start Small (Pilot Agent): Use AI for a standalone, low-stakes use case—like summarizing documents or answering FAQs.

Make It Useful (Departmental Agent): Integrate the agent into real team workflows. Example: triaging insurance claims or reviewing clinical notes.
Scale It Up (Enterprise Platform): Connect AI to your key systems—like CRMs, EHRs, or ERPs—so it can automate across more processes.

Think Big (Federated Intelligence): Link agents across departments to share insights, reduce duplication, and make smarter decisions faster.

What to measure: Track how many tasks are completed with AI assistance versus manually. This shows real-world impact beyond just accuracy.

Closing Thoughts: Domain is the Differentiator

The next phase of AI isn’t about building smarter agents. It’s about building agents that know your world.

Whether you’re designing for underwriting or diagnostics, compliance or production—your agents need to understand your data, your language, and your context.

Ready to Build Your Domain-Native AI Agent?

Talk to our platform engineering team about building custom-trained, domain-specific AI agents.

Further Reading: AI Code Assistants: Revolution Unveiled