Your team knows AI should be part of the product. The question is where. We find the use case that pays for itself, build the pipeline, and put it in production — with monitoring, guardrails, and a team that can maintain it.
The board asks for an AI strategy. Your engineers tried a proof-of-concept that worked on their laptop but fell apart in production. Meanwhile, the manual process keeps eating hours.
The gap between an AI demo and an AI product is engineering. Retrieval pipelines that actually find the right document. Guardrails that catch bad outputs before users see them. Monitoring that tells you when the model drifts. We build all of it, and we have shipped it in production for legal tech, e-commerce, and healthcare clients.
Not research projects. Production systems your users interact with daily.
A RAG pipeline that indexes your documents, contracts, or product catalog and returns the right context to the model every time. Not keyword search with a language model bolted on — proper vector embeddings, reranking, and chunking strategies tuned to your data.
"They have stayed involved as the product evolved, helped us think through new requirements."
— Shea, Founder, Cybersecurity Awareness Training Company
VerifiedDocument classification, form extraction, data entry, support ticket routing. The tasks that eat your team's time and produce errors when someone is tired. We build pipelines that process, validate, and route — with a human in the loop only when confidence is low.
"Their delivery schedule consistently hit the commercial ambition of our business."
— Iain Dickson, CEO, Hero
VerifiedChatbots trained on your product catalog. Conversational agents that browse, recommend, and process orders. Computer vision systems that analyze video in real time. Not generic wrappers around GPT — purpose-built interfaces that understand your domain and your users.
"We love the new dashboard; it looks amazing."
— Head of News Company
VerifiedNo six-month research phase. We validate fast, build incrementally, and measure everything.
We start with your data and your users. What do people ask for that a human currently answers? What process takes the most time? We pick the highest-value use case, build a working prototype in two weeks, and test it against real inputs. If it works, we harden it — proper retrieval pipelines, guardrails for edge cases, latency optimization, cost controls. For workflow automation, we also leverage Microsoft Power Platform to ship integrations and bot flows faster. Then we wire it into your product and set up monitoring so you know when the model drifts or the retrieval misses.
"Their knowledge and experience were instrumental to the project's success."
— Evelyn Ackah, Founder & Managing Lawyer, Ackah Business Immigration Law
Verified on ClutchNot prototypes that lived on a laptop. These run in front of real users every day.
A conversational commerce platform where the AI is trained on the client's product catalog. Customers ask questions in natural language, browse products visually, add items to cart, and check out — all within one chat interface. We built the training pipeline, the retrieval system, and the commerce integration.
Read the full storyFuzzy needed a scheduling tool that lives inside Google Calendar. We built a browser extension where users create meeting polls, participants vote on time slots, and the event auto-generates when everyone agrees. The recommendation engine suggests optimal slots based on invitee availability.
Read the full storyAn immigration law startup wanted to automate eligibility screening and form preparation — work that previously required hours of attorney time per case. We built a RAG pipeline over immigration policy documents, fine-tuned a model for form extraction, and created an end-to-end system that processes applications in minutes instead of hours.
Read the full storyEvery system we build includes the infrastructure around the model: retrieval pipelines, guardrails, latency optimization, cost controls, and monitoring. The model is the easy part. The system around it is the product.
Not every problem needs AI. We spend two weeks testing the use case with real data before committing to a full build. If a rules-based system does the job, we tell you. If AI adds real value, we show you the numbers.
We use open-source models and standard infrastructure. No proprietary black boxes, no vendor lock-in. Your engineers can maintain, retrain, and extend everything we build. We document the architecture and train your team before we hand off.
A working prototype in two weeks. Production-ready in six to twelve weeks depending on complexity. RAG pipelines and chatbots ship faster. Computer vision and custom model training take longer.
OpenAI, Anthropic, open-source models like Llama and Mistral, and custom fine-tuned models when the use case demands it. We pick based on your latency requirements, data privacy needs, and cost constraints.
We can run models on your infrastructure, use self-hosted open-source models, or work with providers that offer data processing agreements. We never send sensitive data to third-party APIs without explicit agreement from your team.
We build monitoring and guardrails from day one. Output quality is tracked continuously. Confidence thresholds route uncertain answers to humans. When performance degrades, we retrain or adjust the retrieval pipeline. You do not discover problems from angry users.
Tell us what the manual process is, how often it runs, and what goes wrong. We will come back with a use case assessment and a two-week prototype plan.