AI Assistants
Product or support assistants grounded in your own data and docs.
We integrate AI where it removes real friction - support, search, automation, insights - grounded in your data, not a generic chatbot bolt-on.
Most 'AI features' are a chat widget stapled onto an existing product. We start from your actual bottleneck - slow support, manual data entry, buried insights - and design AI features that measurably remove it.
Every integration is grounded in your own data and evaluated against real outcomes, with clear guardrails so AI assists your team instead of quietly making mistakes at scale.
Staff spend hours on tasks that pattern-match well to automation.
Ticket queues grow faster than your support team can.
Decisions get made on gut feel because analysis takes too long.
Off-the-shelf AI tools don't understand your product or data.
We identify the highest-leverage use case first, then build AI features grounded in your own data with clear guardrails and human oversight where it matters.
Automation handles repetitive tasks so your team can focus elsewhere.
AI-assisted support that reduces ticket backlog and wait times.
Natural-language access to data your team used to wait days for.
AI features built on your actual data and product context.
Evaluation and oversight built in, not an afterthought.
Every integration tied to a specific, trackable outcome.
Product or support assistants grounded in your own data and docs.
AI-assisted automation for repetitive, rules-based tasks.
Natural-language search across your product or knowledge base.
Forecasting and anomaly detection built on your historical data.
Testing and oversight to keep AI outputs reliable and safe.
Integration with leading LLM providers or your preferred stack.
Highest-leverage AI opportunities identified and prioritized.
Your data reviewed for readiness and grounding quality.
A working prototype tested against real scenarios.
Feature built into your product or internal tools.
Accuracy, safety and cost evaluated before full rollout.
Ongoing monitoring and iteration post-launch.
Every project is staffed with people who've done this before, not a rotating cast of trainees.
We choose technology that fits your needs and won't be unsupported in two years.
Weekly updates, honest timelines and a direct line to your project lead.
Most engagements don't end at launch - we stay involved as things evolve.
Ideally yes - grounding AI in your own data produces far more reliable results than a generic model alone.
We build evaluation suites and guardrails specific to the use case, with human review for higher-stakes decisions.
We're provider-agnostic and typically work with OpenAI, Anthropic and open-source models depending on requirements.
No - chat is one possible interface; we also build automation, search and analytics features that don't involve a chat window at all.
It varies with use case complexity - we scope a fixed-price pilot before committing to a larger rollout.
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