Entalogics

Entalogics reports recognition including Top Rated Plus, Clutch, and GoodFirms. Its teams work across hubs in Lahore, Islamabad, and Karachi, with full US business-hours coverage for US clients.

Working principles

• Senior engineers own architecture, threat modeling, security gates, and code quality.
• AI handles repetitive execution such as drafts, tests, documentation, schemas, and regression sweeps.
• Every line of AI-generated code is reviewed, refined, and validated by a senior engineer before release.
• The same team can cover design, development, DevOps, security, and embedded engineering from kickoff to production.
• Clients retain full code ownership and intellectual property, receive full documentation, and are not locked in.
• Entalogics emphasizes transparency and may push back when a feature or approach does not make sense.

Engagement models

Entalogics supports fixed-scope delivery, dedicated teams, and monthly retainers. The recommended model depends on roadmap certainty, speed requirements, and the client's internal team setup.

Clients work directly with senior engineers and product-minded specialists without heavy management layers. The first step for a project is a technical discovery call covering goals, users, scope, and constraints, followed by a practical plan with timeline and delivery phases.

Contact

Entalogics offers a free 30-minute call with a senior engineer. Contact the company at hello@entalogics.com; the website states that email replies arrive within 24 hours. WhatsApp and Cal.com booking are also available through the website.

• SaaS platforms: Cloud-native, multi-tenant products with billing, dashboards, onboarding, and APIs.
• Web applications: Marketing sites and full-stack platforms using technologies such as React, Next.js, Astro, and Vercel.
• Mobile apps: Cross-platform and native iOS and Android applications using Flutter, React Native, Swift, or Kotlin.
• Desktop applications: Production-grade Windows, macOS, and Linux applications with code signing, auto-updates, and end-to-end distribution.
• Custom Chromium browsers: Branded Chromium or Brave forks with privacy controls, built-in wallets, AI panels, enterprise features, or Web3 capabilities.
• AI-powered products: LLM applications, agent workflows, RAG pipelines, intelligent automation, and AI-native products.

Additional services include:

• UI/UX design with research-led UX, design systems, accessibility, and developer-ready Figma files.
• Cloud and DevOps services, including CI/CD, cloud infrastructure, observability, infrastructure as code, and scaling.
• Post-launch support with monitoring, incident response, and ongoing development through a monthly retainer.
• Custom automation scripts, bots, and workflow integrations.
• Cybersecurity audits, penetration testing, vulnerability assessment, hardening, and compliance readiness.
• Dedicated senior engineering teams embedded with client teams.

Entalogics carries work from kickoff to production with one team rather than handing design or infrastructure to a partner studio.

What the audit checks

• Exposed API keys, tokens, credentials, and secrets in code, configuration, Git history, or client bundles.
• SQL, NoSQL, and command injection, XSS, and unsafe input handling.
• Vulnerable, outdated, abandoned, or hallucinated dependencies and related supply-chain risks.
• Cloud misconfigurations, open buckets, permissive CORS, production debug mode, and missing security headers.
• Broken authentication, authorization failures, and privilege escalation.
• Rate limiting, data exposure, over-verbose API responses, and abuse paths.
• Storage, encryption, and data flows relevant to personal-data handling.
• Prompt injection, unsafe LLM output handling, and exposed model API keys.

Findings map to the OWASP Top 10 and are severity-ranked with proof of concept and a prioritized remediation roadmap.

Process and deliverables

1. Day 1: Scope confirmation, read-only repository access under NDA, and codebase and technology-stack mapping.
2. Days 2–4: Automated SAST and dependency scanning followed by manual review of authentication, business logic, and AI-specific patterns.
3. Day 5: Findings report, fix roadmap, and a live 30-minute walkthrough with the auditing engineer.
4. Optional remediation: Fixed-price fixes by the same team, followed by a re-test.

Deliverables include a findings report, one-page risk map, remediation roadmap, fixed-price fix proposal, and debrief call.

Pricing

• Small codebases: From $499, fixed price, with delivery in 5 business days.
• Larger products: Custom fixed quote for deeper review, targeted testing, compliance requirements, or multiple services.

The audit requires read-only repository access. Entalogics states that it does not need production access or customer data, and repository access is revoked when the audit ends.

AI engineering approach

• Evaluation first: Entalogics builds evaluation datasets before launch and tracks accuracy, cost, and provider tradeoffs.
• RAG architecture: Retrieval-augmented generation grounds answers in client documents or databases and can provide source citations.
• Cost controls: Token spend is modeled before launch, with tiered fallbacks to manage model costs.
• Infrastructure ownership: Products can use self-hosted, VPC, hybrid, frontier API, or open-source model deployments depending on requirements.
• Human review: Higher-stakes outputs can pass through a review queue, with reviewer feedback added to evaluation datasets.
• Monitoring: Accuracy, latency, cost, and refusal rate are tracked through live dashboards.

Product types

• RAG applications for knowledge-base Q&A, document search, and support bots.
• AI agents that use tools, call APIs, plan work, and handle multi-step tasks.
• LLM-powered SaaS features such as summarization, classification, and generation.
• Document intelligence using OCR, document parsing, contract analysis, and medical record extraction.
• AI copilots and assistants for internal legal, operations, or support teams.
• AI-native mobile and web applications.

Entalogics works with OpenAI, Anthropic Claude, Gemini, HuggingFace, Ollama, Llama, Mistral, and other open-source models. Supporting technologies include Pinecone, Chroma, Weaviate, pgvector, LangChain, LlamaIndex, Semantic Kernel, LangGraph, FastAPI, Python, and Postgres.

Engagement options

• AI MVP build: A fixed-scope production AI product typically delivered in 6–10 weeks, with production-ready code and evaluations.
• Embedded AI team: Two to four engineers embedded with the client team on a month-to-month basis.
• Enterprise AI build: Compliance-oriented architecture with options such as self-hosted or VPC deployment, audit logs, RBAC, and data governance.

AI can also be added to an existing product through LLM features, agents, or a RAG layer without rebuilding the product.