Data grounding & retrieval
Connecting the LLM to your actual documents, records or database via retrieval so responses are based on real data, not general training knowledge.
AI Web Applications
Web Applications Backed by LLMs, Not Just Plugged Into One
We build internal tools and customer-facing applications where an LLM is doing real work — summarising, drafting, classifying, answering — grounded in your data and wired into your actual workflows.
LLM features that are a wrapper around a generic API call
Many 'AI-powered' internal tools are a thin interface calling a general-purpose model with no access to the company's actual data, no guardrails on what it can say, and no fallback when it gets something wrong — which works in a demo and falls apart in production use.
- LLM calls with no grounding in company data, producing plausible-sounding but incorrect answers
- No access control layer, so the model can be prompted into revealing data a user shouldn't see
- No logging or review process, so bad outputs go unnoticed until a user complains
- Cost and latency not planned for, causing the feature to be slow or unpredictably expensive at real usage volume
LLM functionality grounded, access-controlled and monitored
We design the data pipeline that grounds the LLM in your actual records or documents, apply the same role-based access controls as the rest of the application, and build logging and human-review paths for outputs that matter — so the AI feature is a reliable part of the workflow, not a liability.
What's included
Service components
Role-based access on AI outputs
Ensuring the model only surfaces data the requesting user is permitted to see, matching the application's existing permission structure.
Workflow-embedded AI features
Summarisation, drafting, classification or extraction built into the actual task flow, not a separate chat window.
Guardrails & output validation
Constraints on what the model can output, with validation logic for structured tasks like data extraction or classification.
Human-in-the-loop review paths
Review or approval steps for AI outputs where errors carry real consequences.
Cost, latency & logging monitoring
Tracking token usage, response times and output logs so cost and reliability are visible, not discovered at the end of the month.
Process
How we deliver it
- Step 01
Use case & data assessment
Identifying which tasks genuinely benefit from LLM assistance and what data would need to be retrievable to ground it.
- Step 02
Architecture & guardrail design
Designing the retrieval pipeline, access controls and validation logic before writing application code.
- Step 03
Build & integration
Embedding the AI feature into the actual application workflow, not a bolted-on chat panel.
- Step 04
Testing against edge cases
Testing for incorrect, ambiguous or adversarial inputs, and confirming access controls hold under the AI layer too.
- Step 05
Launch & ongoing monitoring
Tracking usage, cost, latency and output quality after launch, with a plan to iterate on prompts or retrieval as real usage reveals gaps.
Outcomes
What you get out of it
Outputs grounded in your actual data
Answers, summaries and classifications based on real records, not the model's general training data.
Permissions that hold under the AI layer
The same access rules apply whether a user queries the database directly or through the AI feature.
Predictable cost and performance
Usage, latency and cost monitored from launch instead of discovered as a surprise invoice.
Review paths where it matters
Human oversight built in for outputs with real consequences, rather than blind trust in every response.
Why D2S
Why teams choose us for this
- We scope LLM use cases honestly, including telling you when a deterministic feature would be more reliable than an AI one
- Data grounding and access control designed together, not treated as separate concerns
- In-house AI integration and application development teams working on one build
- Monitoring and iteration built in post-launch, not left as an unplanned ongoing cost
Industries
Sectors we work with
B2B SaaSFinancial services & fintechHealthcare & healthtechLegal & professional servicesLogisticsCustomer support operations
FAQ
Frequently asked questions
01How is this different from just calling an API like OpenAI's directly?
Calling the API is the easy part. The work is in grounding it in your actual data, applying your existing access controls, validating structured outputs and building fallback and review paths — that's what makes it reliable in production rather than just a demo.
02Can the AI feature access sensitive company or customer data safely?
Only within the same role-based access rules as the rest of your application — we design the retrieval and permission layer specifically so the AI feature can't surface data a given user shouldn't see.
03What happens when the model gets something wrong?
We build validation logic for structured tasks and human-review paths for higher-stakes outputs, plus logging so incorrect outputs can be caught and the prompt or retrieval logic refined.
04Will this be expensive to run at scale?
We model expected usage and monitor token cost and latency from launch so you have real numbers, and we design prompts and retrieval to control cost rather than leaving it open-ended.
05Can you add this to an existing application, or does it need a rebuild?
In most cases we can add grounded LLM features to an existing application's workflow without a full rebuild, depending on how accessible the underlying data currently is.
Keep exploring
Related services
Want an AI feature that's grounded in your real data, not a generic wrapper?
Tell us the workflow you want to add it to and we'll assess what's realistic.