AIthatactually answers.
Retrieval, evaluation and guardrails — the parts that decide whether an AI feature works, rather than which model it calls.
Retrieval, not the model
Most disappointing AI fails long before the model.
A question about a contract, run against a real corpus. Every stage below is one we build and evaluate separately — turn one off and watch what the answer becomes.
“What is the liability cap in this agreement?”
GroundedClause 7.2 caps liability at the fees paid in the preceding twelve months, and excludes indirect loss.
Grounded, and it cites where it came from.
Every stage, evaluated
We build an evaluation harness before we tune anything, so a change that helps one question and breaks nine others is caught on the day it is made — not in the demo.
01Use cases
Three situations this is built for.
If one of these sounds like your week, this is the practice to start with.
Sound like something else?
Describe it to an engineer
A support team answering the same thing daily
- 1The situationThe same twenty questions, answered by hand, while the genuinely hard tickets wait behind them.
- 2What we buildA retrieval assistant over your help content and order data that cites its sources and hands off when unsure.
- What you walk away with
- Answers with citations back to your docs
- A graceful hand-off to a human
- Every conversation logged for review
02Capabilities
Where the intelligence actually goes
Model choice is the easy part. These six are where an AI feature is won or lost — retrieval, evaluation, guardrails and the honest measurement behind them.
Machine Learning Models
We start by establishing whether a model is the right answer at all, then train or fine-tune on your data with a held-out evaluation set so improvements are measured rather than felt. Models are versioned and monitored in production, because accuracy drifts as the world moves.
What lands in your repo
- A baseline and evaluation harness before any training
- Versioned models with reproducible training runs
- Drift monitoring and a documented retraining trigger
03Under the hood
The pipeline behind the answer
Hybrid AI pipeline combining pre-trained LLMs with fine-tuned local models for privacy and performance.
Scroll to pull the layers apart
Inference Engine
01Orchestration layer managing prompts, context windows, and model fallback strategies.
LangChainPythonFastApiVector Store
02High-dimensional database for semantic search and Retrieval-Augmented Generation (RAG).
PineconeMilvuspgvectorModel Layer
03Fine-tuned models deployed on GPU clusters for specialized tasks (Vision, Classification).
Hugging FacePyTorchCUDA
04Engagement
From use case to production model
We establish a baseline before we build, so every improvement afterwards is measured rather than claimed.
- 01
Data Prep
Cleaning, labeling, and vectorizing datasets.
- 02
Training
Fine-tuning base models on domain data.
- 03
Integration
Connecting AI endpoints to the main app.
- 04
Evaluation
Testing against benchmarks and edge cases.
- 05
Deployment
Model serving with auto-scaling GPUs.
05Related work
Built in the same neighbourhood.
PlatformFlowCRM AI
AI-assisted CRM with pipeline, deals and sales analytics.
Pipeline · DealsView case
PlatformStockPilot AI
Inventory platform with suppliers, orders and demand forecasting.
Stock · SuppliersView case
PlatformAstraFlow Command
Logistics platform for shipments, warehouses and customers.
Shipments · WarehousingView case
WebsiteNova Nexus
Smart-city platform spanning transport, utilities and public services.
Transport · UtilitiesView case
06Connects with
Rarely bought on its own.
One team runs all seven practices, so the handovers that usually cost you weeks simply do not happen.
How it usually goes
Two seams, and your brief crosses both. Weeks go into getting three companies to agree on what was already decided.
How it goes here
One contract, one team, one running record of every decision. Nothing is re-explained, because nobody new arrives.
- Backend & CloudPipelines, vector stores and the infrastructure to serve models.
- SaaS Platforms & MVPWhere the feature usually belongs — inside your product.
- UI/UX DesignDesigning for answers that are sometimes wrong.
Hover one to find it in the band. Everything shaded is a practice this studio runs — you are never handed to somebody else to finish the job.
Ready to put AI to work?
Tell us what you are building and a lead engineer replies — no SDRs, no discovery-call funnel.
- Fixed-scope or embedded with your team
- NDA before any technical detail
- 100% of the IP transferred to you
