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Strategy
Direction before development, scoped for return rather than for hours.
AI accelerates how fast we build. Senior engineers decide what is worth building, and own it end to end. That combination is what takes an enterprise AI system out of the sandbox and into production, with the governance your security and compliance teams need.
Trusted By
750+ happy clients, including top Fortune 500 companies
Why Sphinx Solutions
Custom AI and software development services that accelerate innovation for smarter businesses.
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Direction before development, scoped for return rather than for hours.
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Roadmaps with milestones tied to business KPIs, not to feature counts.
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Manual bottlenecks removed at enterprise scale, with oversight kept.
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Applied and production-grade, not a lab experiment that never ships.
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Architectures built to extend, not to be rebuilt again in a year.
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Shipped, monitored and owned end to end by the team that built it.
AI Use Cases
Explore real-world AI solutions for the enterprise and find the ones that fit your biggest challenges.
Multi-step autonomous workers that execute business tasks with oversight.
Content, code, and design generation tuned to your brand and data.
Conversational AI systems that resolve your customers’ problems.
Extract, classify, and act on unstructured business documents at scale.
Forecast demand, churn, and risk before they hit.
Personalisation that moves your key metrics, not just clicks.
Ask your company’s own knowledge base a question and get an answer.
Detection, inspection, and monitoring built on visual data.
Orchestration layers that connect agents, RPA, and human review.
AI solutions
Engineering services stated as what they change for your business, not only as what we build.
The entry point for most engagements. We work out where AI actually pays off in your operation, whether your data is ready to support it, and what the first system should cost before you commit a build budget. You leave with a scoped, costed roadmap and a use case ranked by return, not a deck.
Production applications built on LLMs and machine-learning models: copilots, assistants, document intelligence, computer vision and predictive engines. Generic LLM wrappers plateau within weeks, so ours ship with retrieval, memory, tool use, evaluation and guardrails from the first release.
Agents that plan, execute multi-step work across your systems, and recover when a step fails. Autonomous where that is safe, and checked by a person wherever it is not. This is where RPA and AI meet: rule-based bots for the predictable path, agents for the judgement calls, and one audit trail across both.
AI inside our own delivery, on your project. It generates first drafts, exercises code paths and surfaces patterns faster than a team can by hand, while senior engineers review every output and stay accountable for what reaches production. You get the speed without inheriting the risk.
Private model deployment inside your VPC or on-premise, role-based access on every retrieval path, prompt and output logging, and human review where decisions carry consequences. Built to align with the HIPAA, GDPR and SOC 2 requirements your auditors will ask about.
Connected device platforms that turn sensor data into decisions in real time, from edge firmware through to the dashboards your operators use on the floor.
Bespoke platforms designed around your operations rather than around a template. Architected to scale, documented to hand over, and built to outlive their first release.
Native and cross-platform apps for iOS and Android, tuned for real-world performance on the devices your customers actually carry.
Smart contracts, wallets, and distributed ledgers, applied where verifiable trust genuinely earns the added complexity, and not where it does not.
Storefronts and marketplaces built to convert, with AI-driven merchandising, search, and recommendations wired in from the first sprint.
Our philosophy
AI gives us speed. Our engineers give you judgement, accountability, and a system that still works when the request does not fit the pattern. That applies to the agents we build for you too: autonomous where it is safe to be, checked by a person wherever it is not.
Speed · scale · pattern-finding
Accelerates the build
Generates first drafts, tests code paths, and surfaces patterns across data at a speed no team could match by hand.
Faster, without the risk
You get delivery speed with the judgement, traceability and ownership that a model on its own cannot provide.
Judgement · context · ownership
Decides what is right
Engineers review every output, make the architecture calls, and stay accountable for whatever ships into production.
How we work
Most enterprise AI spend does not fail on the model. It fails on execution, which is why our engagements are structured around decisions and sign-offs rather than experiments.
See the full delivery process01
Defined phases rather than open-ended experimentation. Decisions get made early, where they are cheap, instead of drifting into the build.
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Roadmaps, architecture blueprints and working software at every stage, so progress is something you can open rather than something you are told about.
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Named review points and sign-off criteria before anything reaches production, including evaluation, bias and security testing on every model.
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The business metric the system is meant to move is agreed before the first sprint, then tracked after launch rather than assumed.
We build AI agents that carry on learning once they are live, so the systems keep pace with the business rather than freezing at whatever the launch spec happened to say. What we report back is whatever actually shifted: how fast work ships, what it costs to deliver, and whether clients stay. The figures below come from engagements we are running now.
Tech Stack
Client Testimonials
Trusted by founders, CTOs, and product leaders at companies building the future with AI.
Case Studies
We work with you from the first brainstorm through to the final breakthrough. These are the projects where strategy turned into something that shipped and scaled.
Now used by 74 million+ wine lovers globally. Source: Vivino
VIVINO
A platform where wine lovers could scan any wine label, instantly access ratings, reviews, and pricing, and buy directly from vetted merchants. No reference existed in the market.
To build an OCR-powered wine platform without references, holding accuracy and performance while scaling for a growing global audience.
An iOS and Android app with OCR label scanning, ratings, reviews, pricing aggregation, recommendations, and a scalable backend supporting global users, a shopping cart, and wishlists.
Now powering thousands of EV users across locations.
ENERGI BHARAT
A need for one platform where EV drivers could locate charging stations, check availability, and manage charging without juggling fragmented systems.
To handle real-time station data, location tracking, and payments while staying reliable across a wide range of devices and connectivity conditions.
A cross-platform mobile app with real-time station discovery, live status updates, secure payments, user dashboards, and a backend built for growing EV infrastructure.
NO STRESS IMPRESS
AI-powered student productivity and mental wellness platform
Students struggled to balance academics, deadlines, and mental well-being. Existing tools were too generic or too impersonal, which meant low engagement and little effect on stress.
To build an AI-driven platform that manages tasks and schedules while reading behaviour, stress patterns, and productivity gaps, and still feels simple to use.
An AI-powered mobile platform with task planning, mood tracking, and behavioural insights, using recommendations and analytics to lift productivity, engagement, and consistency.
Industries
Healthcare
Clinical workflows, imaging, and patient data handled to the standard regulators expect.
Automotive
Connected mobility, manufacturing systems, and customer experiences built on live vehicle data.
Banking & Finance
Payments, onboarding, and risk tooling that answers to auditors as readily as to customers.
Education
Learning platforms and assessment tools shaped around how people actually study.
Media & Entertainment
Streaming, rights management, and content pipelines that scale with the audience.
Retail & E-Commerce
Storefronts, inventory, and personalisation wired to move revenue rather than clicks.
Travel & Transport
Booking engines, fleet visibility, and pricing systems that survive peak season.
Logistics & Supply Chain
Tracking, routing, and warehouse systems that keep freight and data in step.
Resources
UI/UX design
What separates an app people tolerate from one they reach for without thinking.
Anand Mahajan
20 Jan 2022
App Development
Scheduling looks simple until you meet time zones, recurrence, and sync conflicts.
Anand Mahajan
20 Jan 2022
UI/UX design
The smallest component on the page, and the one most often blamed for a lost sale.
Anand Mahajan
20 Jan 2022
Connect with us
A 45-minute working session with our AI architects. You leave with a scoped approach and an honest view of whether it is worth building, not a sales pitch. Senior engineers only.
What security, compliance and engineering leaders ask us before an AI system reaches production. Can’t find your answer? Get in touch.
Your data stays inside your environment. We deploy open-weight models inside your VPC or on-premise, or use a commercial API under a zero-retention agreement, and in neither case is your data used to train an external model. Retention, redaction and data residency are settled at architecture time rather than patched in afterwards.
Yes. Private deployment is the default for regulated clients: the model, the retrieval layer and the logs all sit inside your network boundary. Where a hosted model genuinely performs better for a use case, we will say so and show you the trade-off rather than quietly making the choice for you.
By grounding it. Answers come from your own content through a retrieval layer rather than from the model’s memory, every answer carries its citations, and outputs are evaluated against a held-out set before release. Where a wrong answer would carry real consequences, a person reviews it before it acts.
A ranked shortlist of use cases with an estimated return on each, an honest assessment of whether your data can support them, a target architecture, and a costed roadmap for the first build. If the finding is that AI is the wrong tool for the problem you brought us, that is what the document will say.
It depends on scope, on how many systems it has to integrate with, and on how much of your data is usable as it stands. Rather than quote a range that would be meaningless, we scope the work properly and give you a fixed figure. Our AI development cost calculator will give you an indicative estimate in the meantime.
The business metric it is meant to move is agreed before the first sprint, with a baseline taken at the same time. After launch we track that metric alongside adoption and model performance, because a system nobody uses and a system that scores well in evaluation are both easy to mistake for success.
Yes. Most clients stay on a support agreement covering monitoring, model and dependency upgrades, evaluation reruns as your data shifts, and a defined response time. You get a named engineering lead rather than a rotating queue, and we can hand over cleanly to your in-house team whenever you would rather run it yourselves.
16 years of delivery, 200 in-house engineers, and a willingness to push back when a request will not serve you. We would rather lose an argument early than build the wrong thing well. Ask us for references from engagements that resemble yours in scale and domain.