AI Strategy and AutomationProposed offering

Put AI inside the software your customers and teams already use.

We add semantic search, recommendations, assistants and intelligent workflows to existing products and internal systems, behind a model gateway that controls cost, quality and data exposure.

Review your product for AI fit See the architecture

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

Integration architecture

Existing product
AI service layer
Model gateway
Data and retrieval
Evaluation
Observability

Reference design. Components are options, not a statement of what is deployed at any customer.

Problems this service is built to solve

These are situations we expect buyers to recognise. Each one states why it happens, what it costs and how we would approach it.

SaaS products losing differentiation

A project management product has the same features as five competitors. A competitor launches natural language reporting, and prospects start asking why your product cannot answer simple questions about their own data.

Why it happens: Feature parity is easy to copy. Differentiation now comes from using customer data intelligently inside the workflow.
What it costs: Longer sales cycles, pricing pressure and churn to products that appear smarter.
How we approach it: We pick features that exploit data only you hold, such as natural language queries over a tenant's own records, and build them with tenant isolation, evaluation and fallbacks so they stay reliable.
What to measure: Feature adoption, retention of adopting accounts, and win rate against named competitors.

Poor information discovery

A customer searches your catalogue for a part by describing what it does. Keyword search returns nothing because your data uses model numbers, and the customer calls support.

Why it happens: Keyword search matches words, not meaning, and product data is rarely written in the words customers use.
What it costs: Lost sales, support calls and poor conversion on long tail items.
How we approach it: We add hybrid search combining keyword and semantic retrieval, query understanding and ranking tuned on your click and purchase data, with evaluation sets to prove improvement.
What to measure: Search success rate, zero result rate, click through on first results and conversion from search.

AI tools disconnected from existing software

Staff paste records into a public chatbot to summarise them, then paste the result back. The summary is not linked to the record, and sensitive data has left the company.

Why it happens: Sanctioned systems offer no AI, so people use unsanctioned tools.
What it costs: Data leakage risk, inconsistent output and no audit trail.
How we approach it: We embed AI actions inside the application, such as summarise this case, with server side data access, redaction rules, logging and storage of results against the record.
What to measure: Share of AI use inside sanctioned systems, redaction coverage and records with stored AI output.

Unpredictable AI costs

A summarisation feature launched to all users. Usage doubled in a month, a few large documents consume most of the spend, and finance cannot attribute the bill to features or tenants.

Why it happens: Costs scale with tokens, and prompt and context size are rarely controlled.
What it costs: Margin erosion and emergency feature limits.
How we approach it: A model gateway that meters by tenant and feature, enforces quotas, routes by task complexity, caches repeated work and limits context.
What to measure: Cost per request, cost per active tenant, gross margin of AI features and cache hit rate.

Solutions we engineer

Concrete capabilities, each with the need it serves, how it integrates, what you receive and the value to expect.

AI enabled product features

Summarise, draft, classify, extract and answer features triggered where the user is working, with human edit before commit.

Customer need: Add intelligence that fits the workflow.
Integration: Application APIs and UI components.
Deliverable: Feature set with design, code and tests.
Business value: Differentiation and productivity.

Semantic and hybrid search

Embeddings and keyword search combined, with filters, permissions and ranking evaluation.

Customer need: Help users find things by meaning.
Integration: Your database, search engine or vector store.
Deliverable: Search service and evaluation set.
Business value: Higher search success and conversion.

Recommendations

Item and user similarity and rules, starting simple and measuring lift with experiments.

Customer need: Surface relevant items or next actions.
Integration: Event and catalogue data.
Deliverable: Recommendation service and experiment plan.
Business value: Engagement and revenue lift, measured.

Model gateway

A single entry point with routing, quotas, caching, redaction, logging and provider failover.

Customer need: Control cost, quality and data exposure centrally.
Integration: All AI features and model providers.
Deliverable: Gateway service and admin console.
Business value: Predictable spend and portability.

Intelligent workflows

Classification, prioritisation and routing steps inside workflows, with confidence thresholds and review.

Customer need: Embed AI decisions in operational processes.
Integration: Workflow engine and ticketing.
Deliverable: Workflow steps with monitoring.
Business value: Faster handling of routine decisions.

AI observability

Tracing, quality sampling, cost and latency dashboards and alerts.

Customer need: Know what the AI is doing in production.
Integration: Existing monitoring stack.
Deliverable: Dashboards, alerts and review process.
Business value: Early detection of regression and cost drift.

How we solve it

A delivery sequence that includes model selection, evaluation, data governance and human oversight.

Identify the product moments

Review user journeys and data to find moments where AI removes effort or reveals information users cannot easily find.

Assess data and architecture

Check which data can be used, how tenants are separated and where the AI layer fits in your architecture.

Design interaction and controls

Define the user experience, the review step, fallback behaviour and the data that may enter prompts.

Choose models and build the gateway

Test models on your tasks, set routing and budgets, and build metering and caching.

Implement and integrate

Build services and UI components, connect retrieval and keep prompts and configurations in version control.

Evaluate and secure

Create evaluation sets, test for leakage across tenants and prompt injection, and agree release thresholds.

Release gradually

Use feature flags and cohort rollouts with metrics for adoption, quality and cost.

Operate and improve

Review sampled outputs and costs weekly, retrain or retune where needed, and revisit model choices as options change.

Solution in action: Natural language search and assistant inside a B2B SaaS product

Solution Concept An illustrative scenario. It describes how we would structure the work, not a delivered customer project.

Starting problem

Customers cannot find records or answer questions about their own data without exporting to spreadsheets.

Existing workflow

Users filter manually, export CSV files and build pivot tables. Support handles repeated how do I questions.

Improved workflow

A search box accepts questions and keywords. A retrieval layer filters by tenant and role, a model answers with links to the records used, and usage is metered by tenant. Complex requests are routed to a larger model and simple ones to a cheaper model.

A user types a question in the product.

Systems involved

Application database, search index, model gateway, identity, billing metering.

Data movement

Tenant scoped records and help content. Embeddings are stored per tenant.

Human decisions

Users review answers before using them in reports. Admins set feature access by plan.

Automation opportunities

Indexing on change, routing, caching and metering.

Exception handling

If retrieval returns weak evidence the assistant says it cannot answer and offers to search, instead of guessing.

Resulting user experience

Users ask a question and see the answer with the exact records behind it.

KPIs to evaluate

Search success rate
Answer accuracy on an evaluation set
Cost per active tenant
Feature adoption

What you receive

Concrete deliverables for this service, written so you can check them against the contract.

AI features integrated into your product or system
Model gateway with metering, quotas and caching
Search and retrieval services with evaluation sets
Data handling and redaction rules
Observability dashboards and alerts
Release plan with feature flags and rollback
Security test results including tenant isolation
Documentation for your engineering team

Technology and engineering

Options we would evaluate for this service. Unless a group is marked as publicly listed on kindlebit.com, treat each tool as a proposed implementation option. Naming a tool does not imply a vendor partnership.

AI services (proposed implementation options)

OpenAI compatible APIs
Anthropic
Azure OpenAI
AWS Bedrock
Google Vertex AI

Retrieval

PostgreSQL with pgvector
OpenSearch or Elasticsearch
Pinecone
Qdrant
Weaviate

Application stack

React
Next.js
Node.js
Python
FastAPI
.NET

Observability

OpenTelemetry
LangSmith
Arize
Datadog or Grafana

Relevant Kindlebit work and evidence

We use the strongest evidence available and say which kind it is.

Proposed offering

Evidence status for this service

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

See case study status

Solution ConceptInteractive demo with simulated data

AI powered SaaS product demonstration

The demonstration shows tenant isolated search, an assistant, plan based entitlements and an observability panel with simulated data.

Open the demonstration

Business outcomes and success criteria

These are the measures we would agree before work starts. They are criteria for success, not results from past engagements.

Adoption

Share of active accounts using the AI feature weekly.

Quality

Accuracy on a held out evaluation set and user feedback rate.

Unit cost

AI cost per active tenant and per request.

Latency

Median and 95th percentile response time.

Questions buyers ask

Can we add AI without rebuilding our product?

Usually yes. AI features are typically added as services behind your existing APIs, with new UI components. We review your architecture first to find the least invasive approach.

How do you keep customer data separate?

By enforcing tenant filters at the retrieval layer, storing embeddings per tenant or with tenant keys, and testing for cross tenant leakage as a release criterion.

Will our data train the model?

That depends on the provider and contract. We choose providers and settings that match your policy, document them, and can use endpoints that do not retain or train on your data.

What does it cost to run?

Run cost depends on request volume, context size and model choice. We estimate it during design and build metering so you can see actual cost by feature and tenant.

Can we switch models later?

Yes. The gateway abstracts providers so models can change without rewriting features, and evaluation sets let you compare a new model before switching.

How do you measure whether it worked?

We agree success metrics up front, such as search success or adoption, instrument them and compare against the baseline before release.

Related services

Industries where this applies

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