Integrate LLMs and tool-use into your product or internal tools — including MCP servers that expose your systems to agents.
Discipline: AI Agents
Typical outcome: +312% avg. organic traffic in 12 months
Engagement: monthly retainer or one-time audit
Seniority: lead strategist on every weekly call
About This Service
How We Deliver LLM & MCP Integration
API and tool-use integration done to production standards: streaming, structured outputs, function/tool schemas, caching and rate-limit handling. We also build MCP servers so agents can safely reach your data and actions through a typed, auditable interface.
The patterns we see across new audits — and the fixes baked into every engagement.
AI features nobody can ship
Wanting AI in your product is easy; building it right is hard. We engineer production LLM features.
Runaway token costs
Naive LLM usage burns money at scale. We engineer for cost-efficiency without losing quality.
Unreliable, unsafe outputs
LLM features that hallucinate or misbehave hurt the product. We build guardrails and evals in.
Data privacy and security concerns
Sending data to models raises real concerns. We architect secure, compliant integration.
Measurable Results
Engineered for outcomes, not vanity metrics
Every LLM & MCP Integration engagement starts with a measurable goal — revenue, qualified pipeline, or first-page rankings on your highest-intent terms. We instrument the work end-to-end so you can see the trajectory in week two, not month six.
No retainers without a measurable outcome attached. Every sprint is anchored to a KPI we agreed on in week one — with weekly Loom updates, a shared dashboard, and a senior strategist on every call.
Senior strategist on every account — no juniors learning on your dime
Weekly Loom updates plus a shared Looker dashboard
Your stack, your CMS, your CMS workflow — we adapt to you
Outcome-tied pricing — KPI ranges baked into the contract
Our Process
How we ship LLM & MCP Integration
A proven 4-step framework that compresses time to results without cutting corners.
01
Audit & Diagnose
Benchmark where LLM & MCP Integration stands today, surface the highest-leverage opportunities, agree on the KPIs we'll be measured on.
02
Strategy & Roadmap
A senior strategist sequences the 90-day roadmap with quick wins front-loaded and moat plays scoped behind them.
03
Execute & Ship
The pod ships work weekly — instrumentation, deliverables and dashboards updated against the KPIs you signed off on.
04
Measure & Compound
Monthly reviews recalibrate the roadmap. Wins compound, the program keeps moving, every dollar stays accountable.
Adding an LLM feature to a product is deceptively hard to do well — prompt engineering, context management, streaming, error handling, fallbacks and evaluation all matter. We engineer production-grade LLM features: robust prompts, retrieval and context strategy, streaming UX, and the reliability engineering that makes an AI feature actually work in a shipped product.
Proper engineering is what turns "we want AI in our product" into a feature users trust.
Prompt, context and retrieval engineering
Streaming UX and error/fallback handling
Reliability engineering for AI features
Production-grade in-product AI
02
Organic traffic — last 90d
+263% vs prior period
Cost & performance
Engineer for efficiency at scale
Naive LLM integration burns money and slows down at scale — model choice, caching, prompt efficiency, and routing all determine cost and speed. We engineer for cost-efficiency and performance: right-sizing models to tasks, caching, prompt optimisation and smart routing, so your AI feature is affordable and fast at real volume.
Cost-and-performance engineering is what makes an LLM feature viable beyond the demo.
Model right-sizing and routing
Caching and prompt-efficiency optimisation
Cost control at scale
Latency and performance engineering
03
Content velocity
8 long-form posts · 4 weeks
Safe & secure
Guardrails, evals and secure data handling
LLM features that hallucinate, misbehave, or leak data damage the product and the business — so safety, evaluation and security are core, not afterthoughts. We build guardrails for output quality and safety, evals that measure the feature against real cases, and secure, privacy-conscious data handling and model architecture.
Safety, evaluation and secure architecture are what let you put an LLM feature in front of users and stand behind it.
Output-quality and safety guardrails
Evaluation against real cases
Secure, privacy-conscious data handling
Compliant integration architecture
Real Results
LLM & MCP Integration outcomes that compound
Average measurable wins across active engagements — every number traceable inside the dashboards we ship clients.
Verified across 500+ live client engagements
Reported via live Looker Studio dashboards
Tied to revenue KPIs, not vanity metrics
+312%
Avg. organic traffic in 12 months
37 days
Median time-to-first-page
60+
Verticals shipped against
4.9★
Avg. client satisfaction
FAQs
Questions buyers ask before signing
Quick answers to the things buyers always check first.
What does LLM integration involve?
Engineering production-grade AI features into your product — prompt, context and retrieval strategy, streaming UX, error handling and fallbacks, cost and performance optimisation, guardrails, evals, and secure data handling. It's the difference between an AI feature that works reliably and one that falls over or burns money.
How do you control LLM costs?
By engineering for efficiency — right-sizing models to tasks, caching, prompt optimisation and smart routing — so your AI feature stays affordable and fast at real volume, rather than burning tokens with naive usage.
How do you keep the AI feature reliable and safe?
With guardrails for output quality and safety, evals that measure the feature against real cases, and fallback handling — so it behaves predictably in production and you can improve it, rather than hoping it works.
What about data privacy?
We architect secure, privacy-conscious integration and data handling — appropriate model choices, data minimisation and controls — so sending data to models is done compliantly and safely for your requirements.
No account-manager buffer and no juniors learning on your budget — you work directly with the people who do the work.
Founder
Rao Anas
Founder & CEO
Company vision · technical direction
Founder and CEO. Sets the company vision and leads engineering and technical direction across both SEO and software development — the person who architects the hard problems and owns the standard the whole team builds to.
Founder
Rao Usama
Co-Founder & Product Officer, Head of AI / Data
14+ yrs engineering · AI agents, MCP & automation
Co-Founder and Head of AI & Data. 14+ years in software engineering; leads our AI-agent, MCP-integration and workflow-automation work — turning models into systems that actually run in production.
MSc, Data Science
Nimra Jabbar
Front-End Developer & SEO Specialist
MSc Data Science · front-end · technical SEO
Front-end developer and SEO specialist with a Master’s in Data Science. Builds fast, accessible interfaces and the on-page and technical SEO that make them rank — with experience spanning EdTech, enterprise systems and digital consulting.
MSc, Data Science
Abdur Rahman Shah
Web & SaaS Engineer · DevOps
WordPress · custom web apps · SaaS · DevOps · technical SEO
Builds and ships the web layer — WordPress, custom web apps and SaaS architecture — with the DevOps to run it and the technical-SEO knowledge to make sure what he builds is fast, crawlable and built to rank.
Drop your URL, pick the goal you care about most, and we'll show you exactly where the traffic is leaking — technical, content, or authority. No pitch, no obligation, no sales script.
Every quote below is from a real client engagement. We only report what we can tie to a ranking, a booking, or a pipeline number.
5 avg. client review · 60+ engagements
Slide 1 of 8
Well-researched, well-structured content that aligned perfectly with our goals. Organized, easy to collaborate with, and consistently on time.
Holly Caunt
Content lead
Slide 2 of 8
Our traffic had been flat for a long time. Their strategy was well thought out and we started seeing real momentum soon after implementation.
Jack Evans
Startup founder
Slide 3 of 8
The technical audit uncovered issues we had no idea about. Clear explanations, practical recommendations, and a report that told us exactly what to fix first.
James Williams
Law-firm marketing lead
Slide 4 of 8
Understood the brief after a single call and executed with confidence. Delivered on schedule with excellent communication the entire way through.
Jenny Abouobaia
Agency owner
Slide 5 of 8
They consistently identify high-quality opportunities, keep communication clear, and deliver exactly what we need. A partnership we plan to keep.
Jessica Levenson
Marketing director
Slide 6 of 8
Easy to work with and kept me updated throughout. Delivered quality results exactly as expected, with no back-and-forth needed.
John Carlo N.
SEO consultant
Slide 7 of 8
Every weekly call has a senior strategist on it, not an account manager reading a script. The work shows up in our pipeline, not just a vanity dashboard.
Kate Smoothy
SaaS growth lead
Slide 8 of 8
Genuinely rigorous technical work — they found and fixed foundation issues three previous agencies had missed, and communicated every change clearly.
Lily Ray
SEO consultant
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