Generative AI
Generation with a grounding problem solved
Most generative features fail on trust, not fluency. We build retrieval that is scoped to the asking user, outputs that are validated before they land, and a review path for anything customer-facing.
Capabilities
What this actually includes
The concrete pieces of work, so you can tell what you are buying rather than inferring it.
Tenant-scoped retrieval
Vector and keyword search filtered at the query layer by the caller's identity, so a search can never surface another customer's documents.
Structured output contracts
Model output parsed against a schema and rejected on failure, rather than trusted into a database write.
Grounded citation
Every generated claim traceable to the source passage it came from, surfaced in the UI so reviewers can check it.
Content moderation
Input and output classification on anything user-visible, with a defined escalation path for flagged material.
Prompt and context management
Versioned prompts, bounded context windows and cache-aware assembly that keeps latency and spend flat as usage grows.
Human review workflows
Draft, review, approve queues for regulated or brand-sensitive output, with edit capture that feeds future evaluation.
How we work
The sequence we follow
Qualify the use case
We separate the tasks where generation genuinely helps from the ones where a template or a search box is better and cheaper.
Build the corpus
Ingestion, chunking and permission mapping for your source material — the part that decides whether the feature is useful or noise.
Ground and validate
Retrieval tuned against a labelled question set; outputs constrained to a schema and checked before they are shown.
Pilot with reviewers
A limited user group works the review queue while we measure acceptance and edit distance on real output.
Scale and monitor
Rollout with live quality sampling, cost per interaction tracked per tenant, and a standing regression suite.
Outcomes
What good looks like
Illustrative targets from engagements of this shape. Yours get agreed up front and measured.
0%
first-draft acceptance after grounding work
0%
reduction in time spent on routine drafting
0
cross-tenant retrieval leaks in scoped search
Toolkit
What we build with
Chosen per engagement against your constraints — never a house stack applied regardless of fit.
Retrieval
- pgvector
- Pinecone
- Elasticsearch
- Hybrid BM25 + dense
Models
- Claude
- OpenAI
- Cohere rerank
- Local embeddings
Validation
- Zod
- Pydantic
- JSON Schema
- Output classifiers
Delivery
- Next.js
- Streaming APIs
- Server-sent events
- Edge caching
Questions
Things clients ask first
Not on the configurations we deploy. We use enterprise API tiers with training disabled and data retention set to the shortest the provider allows, and we document that setting per environment.
Keep exploring
Related capabilities
Tell us what you are trying to build
A short call with an engineer, not a sales team. If we are not the right fit we will say so and point you somewhere better.