Internal knowledge & search
Staff ask a question and receive an answer linked to the relevant documents. We connect approved information sources and preserve access permissions, so people can check the answer and only see material available to them.
AI integration
Help staff find answers in company documents, process incoming paperwork and spend less time copying information between systems. Jolt builds AI into those workflows, with people reviewing the decisions that matter.
AI connected to your operations
Document processing
Turn incoming invoices, forms or reports into structured records your systems can use. We test extraction against your documents and route uncertain or incomplete results to a person, reducing repetitive entry without hiding exceptions.
Product & system integration
Connect the assistant to the software where work already happens. We agree what it may read, suggest or change, with human approval for sensitive actions and records of what happened.
Evaluation & operational controls
A useful demonstration is the start of a test. We check answers on real tasks, compare them with your current process and measure error rates, handling time and running costs. Those results determine whether the system is ready for wider use.
Industry research & examples
Start where AI can remove a real bottleneck
Focus on work that happens every day
McKinsey’s 2023 analysis placed about 75% of generative AI’s potential value in customer operations, marketing and sales, software engineering and R&D. These are modelled opportunities, not achieved savings. We start with a recurring task in your business and test whether AI improves time, quality and cost before expanding it.
McKinsey · 2023 — Read the sourceInternal knowledge at Morgan Stanley
Morgan Stanley reported in June 2024 that 98% of its financial-adviser teams had adopted an assistant giving access to internal knowledge. Adoption does not establish productivity gains, but this is a real deployment of AI around company information. Jolt can build that kind of search around your documents, with source references, access permissions and evaluation against real questions.
Morgan Stanley · 2024 — Read the sourceFeatured Projects
View more projectsGolden Eye
An AI-powered market analysis platform for hedge funds. We brought market data, company relationships and signal analysis together in a web application.
Read the case studyDrowzee
We worked with a scientist to turn raw brain and sleep-tracking data into algorithms and a clear user interface. The work combined specialist research with software development.
Before we start
Where should a business start with AI?
Choose a recurring task with accessible data and a clear definition of success. We assess whether AI is appropriate, define a pilot and compare the results with the current process.
Can you connect AI to our existing systems?
Yes, where APIs or other suitable interfaces are available. We review the systems and their permissions, then agree how information and actions will move between them.
How do you handle sensitive company data?
We agree permitted data, service providers, access and retention requirements before development. These decisions determine the architecture. Privacy and regulatory requirements are assessed for the specific project.
How do you reduce incorrect AI answers?
We use approved sources, test cases, structured validation and human review for important decisions. The system should show its evidence and identify missing information. AI can still make mistakes, so exceptions need a defined handling process.
What will an AI integration cost to run?
The cost depends on usage, model choice, data retrieval and support. We measure a representative workload during the pilot and provide an estimate before expanding the deployment.