Your AI assistant handles code, docs, compliance, and reasoning in the same session. But it treats every input the same way. Prism keeps one shared model and routes each input to the right internal specialist — better results, less sprawl, one system.
Most internal AI workflows mix very different kinds of work in the same session: code, documentation, policy text, factual reference, and analytical reasoning. Teams handle this one of two ways — and both fail.
A single model treats every request as the same kind of problem. Quality drifts. Domain-specific precision suffers. The model is adequate everywhere, excellent nowhere.
Multiple prompts, agents, and tuned models glued together externally. Maintenance overhead grows. Orchestration becomes brittle. Each new domain means another system to manage.
One shared model with lightweight internal specialists. A routing layer detects what kind of work the input requires and activates the right specialty path.
Instead of treating every request as the same kind of problem, Prism identifies what the input needs and routes it to the right internal specialty.
A lightweight routing gate reads each input and detects what kind of work it requires — code, documentation, compliance logic, factual reference, or analytical reasoning.
The system activates the most relevant internal specialist path. These are lightweight domain experts embedded inside the shared model — not separate systems bolted together.
The output returns through one shared interface. Your team gets a single assistant that handles mixed-domain work with domain-specific precision — without managing multiple tools.
Prism is designed for teams whose internal AI workflows span multiple kinds of work in the same session.
Code, specs, design docs, incident history, and troubleshooting — all in one assistant that knows the difference.
Reference material, procedural logic, and exception handling require different modes of reasoning. One generic model misses the distinctions.
Contracts, factual business documents, structured analysis, and memo synthesis — mixed document types that demand different treatment.
Product knowledge, policy rules, and diagnostic reasoning in the same conversation. Support teams need precision across all three.
We do not ask you to commit to a platform. We ask you to pick one workflow, run a benchmark, and see whether the numbers justify going further.
We determine whether you have one workflow that is mixed-domain, valuable enough to improve, and measurable enough to benchmark. If not, we stop here.
We build a held-out benchmark from your real data, reproduce your current baseline, and compare it against routed specialists. You get a benchmark set, a baseline comparison, a short technical readout, and an explicit go/no-go recommendation.
Success bar: At least 10% error reduction on the priority slice, or equal quality with a simpler system shape. If no credible win appears, we stop.
Only if the benchmark passes. We deploy the routed-specialist stack into one real internal workflow in a controlled setting. You get a working pilot, quality and latency analysis, routing and failure analysis, and a production recommendation.
Scope: One workflow. One baseline. No feature creep. If the lift disappears under real usage, we stop.
Only after a successful pilot and explicit acceptance review. Checkpoint management, specialist retuning, evaluation refreshes, and drift monitoring for the approved workflow. No vague retainers — continued engagement requires continued proof.
The system behind Prism has been tested through 30+ experiments across 7 series, with 12 honest stops and pivots preserved in the record. These are internal results — the benchmark sprint tests whether they hold on your data.
These results are internal benchmarks on our current test domains. They do not prove universal gains on every workload. The benchmark sprint exists precisely to test whether these results transfer to your specific workflow. If they don't, we stop.
Pick one mixed-domain workflow where your current AI baseline is underperforming. We will test whether specialist routing improves it — on your data, against your baseline, with explicit success criteria.
Start a Discovery ConversationThe right first conversation is simple: do you have one workflow where this could be tested against your current baseline?
We identify whether you have a mixed-domain workflow worth testing. If not, we stop there.
The first engagement is a bounded benchmark sprint. You see results on your own data before making any larger decision.
If the benchmark does not show a credible path to value, we recommend stopping. No forced pilots. No vague retainers.
Or email us directly at info@suite110.com