Retrieval Augmented Generation (RAG) over your docs, tickets, code, products
Service
AI that moves the metric - not the meeting.
Retrieval, agents, classification, copilots. Applied where they earn their place. Built with evals, guardrails, and governance from day one.
Overview
AI is most valuable when it disappears into a product as a feature that quietly makes the customer's life easier - not as a pop-up "ask the AI" button. We help you find the workflows where AI actually moves a number that matters, prototype quickly with the right model and the right pattern (RAG, agent, classification, extraction, copilot), and ship it with evaluations, guardrails, and a cost model. The result is AI in production - measurable, governable, and durable.
Capabilities
What we deliver
The full surface area of this discipline - pick the slice you need today, or hand us the whole ambition.
AI agents with tool use, planning, and human-in-the-loop checkpoints
Classification, extraction, summarization, translation
In-product copilots - chat, inline suggestion, autocomplete
Multimodal - voice, vision, document understanding
Evaluation harnesses - offline benchmarks and online experimentation
Guardrails - content safety, PII redaction, jailbreak resistance, fact-grounding
Prompt management - versioning, A/B testing, rollback
Cost optimization - caching, distillation, smaller models where they suffice
Data governance - what trains, what doesn't, where it goes, who sees it
Process
Our approach
A predictable rhythm with deliberate decision points - so you always know where we are and what's next.
01
Identify the metric
What AI is supposed to move and by how much.
02
Prototype
Working demo on real data in two weeks.
03
Evaluate honestly
Eval set, human review, win-rate vs baseline.
04
Ship with guardrails
Safety, cost ceilings, observability.
05
Measure and iterate
The model behind the feature should keep improving.
Pipeline
How AI flows through your product
Four steps. Built with guardrails.
Retrieval
Pull the right context from the right source. Vector search, BM25, graph traversal - whichever wins the eval.
Reasoning
Apply the model to the retrieved context. Prompt design, tool use, planning, evaluations on every change.
Action
Execute decisions back into the product. Tool calls, agent actions, in-product copilot output, all with audit trails.
Evaluation
Measure outcomes. Did it move the metric? Win-rate against baseline, cost per call, latency, guardrail hits.
Stack
Technologies we use
Chosen for fit, not fashion. We bring the playbook; your team keeps the keys.
Anthropic ClaudeOpenAIGoogle Vertex AIAWS BedrockLangChainLlamaIndexPineconepgvectorWeaviateBraintrustLangfuseHelicone
Where we work
Industries we serve in this discipline
Healthcare
Government & Public Sector
Media
EdTech
IT Services
Logistics
eCommerce
BFSI & Fintech
Manufacturing & Industry 4.0
Real Estate & PropTech
Travel & Hospitality
Outcome
What you get
An AI feature in production, an evaluation suite that runs on every change, a versioned prompt repository, monitoring and cost dashboards, a governance document covering data flow and retention, and an upgrade plan as the model landscape shifts.
FAQs
Frequently asked
Whichever wins your eval suite on quality, cost, and latency. We'll set the eval up and let the data decide.
Real, but reducible - retrieval grounding, structured output, eval-driven prompt iteration, guardrails. We design for it.
Not on enterprise-tier APIs from major providers. We document this in writing for your legal team.
Models charge per token. We forecast unit economics during prototyping so there's no surprise at scale.
Sometimes. We start with strong base models and only fine-tune or distill when evals justify it.
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Have a goal you want unlocked?
Come to us. We'll turn it into outcomes - with surgical precision.