AI Solutions & Intelligent Automation for Business
AI that actually improves your operations, not just your pitch deck.
Sphere Techlabs designs and integrates AI workflows, agents, and LLM-powered features that solve real problems inside your products and processes. We focus on practical use cases—reducing manual work, speeding up decisions, and making your software feel significantly smarter—without turning your stack into an unmaintainable experiment.
Applied AI, grounded in engineering.
We don't bolt a chatbot onto your product and call it a day. We look at your workflows, data, and systems, then design AI solutions that are reliable, observable, and easy to evolve. That can mean assistants for your team, automation for repetitive tasks, or entirely new capabilities inside your application.
AI-assisted internal tools
Augment your team with tools that understand your context instead of generic prompts.
We build internal assistants for support, finance, operations, sales, and product teams that can search across documents, tickets, and systems; summarize long threads; suggest next actions; and help people move faster without losing control of the final decision.
Workflow automation & agents
Automate multi-step, multi-system workflows instead of just one-off tasks.
We design lightweight agents and automations that can read data, call APIs, trigger events, and coordinate work between tools—while still giving humans visibility and override control. Think onboarding flows, approvals, reconciliation, reporting, or customer follow-ups handled end-to-end.
LLM features inside existing products
Make your product feel smarter without rewriting it from scratch.
We embed LLM-driven features directly into your app: smart suggestions, assisted writing, field auto-filling, categorization, natural-language filters, and "explain this" buttons that help users understand complex data and actions.
Retrieval & embeddings (semantic search, RAG)
Let users (or internal teams) ask questions in plain language and get answers grounded in your own data.
We implement retrieval-augmented generation (RAG) and semantic search over documents, tickets, CRM, product data, or log streams—so the AI can reference the right information instead of hallucinating.
Monitoring, evaluation, and cost controls
If you can't see what the AI is doing, you can't trust it.
We build logging, metrics, evaluations, and cost tracking around AI features so you can monitor behaviour, catch regressions, debug bad outputs, and keep usage within budget as adoption grows.
From idea to measurable impact.
We treat AI work like any other serious engineering project: define success, design the system, ship iteratively, and measure results.
Identify leverage points
We start by mapping your processes and user journeys to find where AI can create the most impact—cutting repetitive work, unblocking bottlenecks, or improving user experience.
Choose the right approach
Not every problem needs a fine-tuned model or elaborate agent.
We evaluate off-the-shelf LLMs, retrieval setups, fine-tuning, tool use, or more traditional automation, and pick the smallest solution that reliably does the job.
Design a safe architecture and guardrails
We design prompts, data flows, permissions, and fallbacks to keep behaviour predictable. That includes validation, rate limiting, red-flag detection, and clear ways to handle errors or uncertain outputs.
Integrate into your stack
We connect the AI solution to your existing systems, APIs, and UIs so it feels like a natural extension of your product—not a bolted-on experiment that lives in a separate tool.
Measure & iterate
We track usage, quality, time saved, and other key metrics. Then we refine prompts, data sources, workflows, and models over time so the solution gets better as your team uses it.
Where we're a great fit
We're a strong match when you care less about "AI for the logo" and more about concrete, compounding improvements.
Manual workflows that are repetitive, error-prone, or depend on copy-paste between tools
Teams spending a lot of time searching, summarizing, or interpreting large amounts of information
Products that could benefit from recommendations, smart search, or assisted actions but need to stay stable and predictable
Organizations that have experimented with AI prototypes and now want a realistic, maintainable roadmap
Engineering leaders who want AI integrated into their systems with proper logging, tests, and rollback strategies