We architect and build retrieval, automation and agent systems that are secure, measurable and maintainable, and we work through the hard parts with you.
AI Solutions & Intelligent Automation
Most AI demos look great and fall apart on real data. We design for the real thing from day one: permission-aware access, evaluation before rollout, and cost and quality you can measure. Models, vector stores and cloud are chosen to fit your environment, not ours.
High-impact AI services
Three areas where well-engineered AI pays off first.
Natural-language answers across your documents, wikis, tickets and code, grounded in your content with citations back to the source. Built with hybrid search, reranking and permission-aware retrieval, so people only see what they’re already allowed to see.
Layout-aware parsing and schema-constrained extraction for invoices, contracts, forms and tickets. Each field carries a confidence score; low-confidence items go to a human review queue, and clean results flow into your ERP, CRM or ticketing system.
A hands-on assessment of data quality, sensitive-data exposure, pipelines and API readiness. You get a prioritized use-case list and a practical roadmap before any build budget is committed.
Responsible AI Framework
Trust is designed in at the architecture level, not added at the end.
Data protection is built into our solution approach through appropriate access controls, secure deployment options and data handling practices aligned with client requirements.
Every answer cites its sources, and we test for groundedness, so unsupported answers are caught and reduced rather than hidden.
Review steps sit where an error would be costly: approvals, escalations and low-confidence cases, with a full record of who decided what.
How we build
A few principles guide every solution we design.
We build a test set from your real examples before we build the solution, so quality is measured against facts instead of impressions.
We choose models and tools for your use case, budget and security needs, and design so you can switch later without a rebuild.
Keyword and semantic search combined with reranking, for answers that are accurate as well as fluent.
Access rules from your existing systems carry through to the AI layer, so people only see what they’re already allowed to see.
AWS, Azure, Google Cloud or your own environment, wherever your data and policies need it to be.
Monitoring, cost tracking, documentation and a clean handover, so your team can run and extend what we build.
The 9Sprints engine
One clear path for every engagement, so you always know where things stand.
We map the workflow, data sources, security needs and risks, and agree on success metrics up front.
We design the architecture, select models and tooling, build an evaluation set from your real examples, and plan the sprints.
We build in short sprints on real data, test accuracy, latency and cost at every step, and review results with you.
We deploy with monitoring and guardrails, hand over full documentation, and keep tuning quality and cost over time.
Engagement models
Begin small, then grow only when the results justify it.
Testing an idea on your own data before committing to a full build.
A working prototype, an evaluation report and a clear recommendation on what to do next.
Custom RAG, automation and agent solutions taken from design to production.
Defined scope and milestones, a documented architecture and a smooth handover to your team.
Adding AI, data and engineering expertise to a team you already have.
Experienced professionals who fit your workflow from the first week.